Mappr (Lisette version)

Scale up evaluation report mapping against evaluation frameworks using agentic workflows

Warning

This notebook is a work in progress.

Manually mapping evaluation reports against IOM’s Strategic Results Framework (SRF) is time-consuming and resource-intensive with ~150 outputs to analyze. Additionally, the mapping process needs transparent and human-readable traces of LLM decision flows that both reflect natural reasoning patterns and allow human evaluators to audit the mapping logic.

A three-stage async pipeline leveraging Global Compact for Migration (GCM) UN General Assembly Resolution objectives as SRF Outputs pruning mechanism:

Stage 1: SRF Enablers & Cross-cutting Analysis

Async parallel analysis of Enablers (7 categories) and Cross-cutting Priorities (4 categories) using shared semaphore for rate limiting
Purpose: Identify if report is primarily meta-evaluation/transversal in nature
Fast processing: ~11 items total with concurrent execution, provides context for subsequent stages

Stage 2: Informed GCM Analysis

Rate-limited parallel processing of GCM Objectives (23 items) informed by Stage 1 results
Condensed representations: UN General Assembly Resolution formulation simplified for retrieval efficiency
Concurrent theme analysis with API quota management

Stage 3: Targeted SRF Analysis

SRF Filtering: Use GCM results + gcm_srf_lut lookup table to prune ~150 SRF outputs to ~20-50 relevant ones
Deep parallel analysis: Full hierarchy context (objective → outcome → output → indicators)
Async batch processing: Final targeted analysis of pruned SRF outputs with retry logic and error handling

Exported source

from pathlib import Path
from functools import reduce
from toolslm.md_hier import *
from rich import print
import json
from fastcore.all import *
from enum import Enum
import logging
import uuid
from datetime import datetime
from typing import List, Callable
import dspy
from asyncio import Semaphore, gather, sleep
import time
from collections import defaultdict
import copy

from pydantic import BaseModel, Field
from typing import List

from evaluatr.frameworks import (EvalData, 
                                 IOMEvalData, 
                                 FrameworkInfo, 
                                 Framework,
                                 FrameworkCat,
                                 find_srf_output_by_id)

#from evaluatr.db_traces import TraceDB, Trace
from fastlite import database

from lisette import Chat, AsyncChat
import json

Exported source

from dotenv import load_dotenv
import os

load_dotenv()
GEMINI_API_KEY = os.getenv('GEMINI_API_KEY')

Exported source

cfg = AttrDict({
    'lm': 'gemini/gemini-2.0-flash',
    'api_key': GEMINI_API_KEY,
    'max_tokens': 8192,
    'track_usage': False,
    'call_delay': 0.1, # in seconds
    'semaphore': 30,
    'dirs': AttrDict({
        'data': '.evaluatr',
        'trace': 'traces'
    }),
    'verbosity': 1,
    'cache': AttrDict({
        'is_active': False,
        'delay': 0.05 # threshold in seconds below which we consider the response is cached
    }),
    'max_iter': 10
})

Exported source

traces_dir = Path.home() / cfg.dirs.data / cfg.dirs.trace
traces_dir.mkdir(parents=True, exist_ok=True)

Exported source

# lm = dspy.LM(cfg.lm, api_key=cfg.api_key, cache=cfg.cache.is_active)
# dspy.configure(lm=lm)

# doc = Path("../_data/md_library/49d2fba781b6a7c0d94577479636ee6f/abridged_evaluation_report_final_olta_ndoja_pdf/enriched")
doc = Path("../_data/md_library/49d2fba781b6a7c0d94577479636ee6f/final_evaluation_report_final_olta_ndoja_pdf/enriched")

pages = doc.ls(file_exts=".md").sorted(key=lambda p: int(p.stem.split('_')[1]))
report = '\n\n---\n\n'.join(page.read_text() for page in pages)
print(report[:1000])

# **PPMi** .... page 1

**Final Evaluation of the EU-IOM Joint Initiative for migrant protection and reintegration in the Horn of Africa**

Final Evaluation Report, 17 March 2023

!(img-0.jpeg)

**EU-IOM** Joint Initiative for Migrant Protection and Reintegration

Project funded by the European Union
Project implemented by IOM

---

This Final Evaluation Report was commissioned by IOM and developed by the evaluation team of PPMI Group, including:
Loes van der Graaf, Rimantas Dumcius, Radvilė Bankauskaitė, Anna Kiss-Pal and Laura Daukšaitė, as well as by 
external expert Anthony Roger Plant. The evaluation team is grateful to all IOM staff and stakeholders to the 
JI-HoA for their time taken to participate in interviews. The team is especially grateful to the returnees, 
migrants, and community members who participated in Focus Group Discussions.

This study was produced with the financial support of the European Union. The contents of this report are the sole 
responsibility of

Hierarchical report navigation

Thanks to toolslm.md_hier and a clean markdown structure of a report markdown, we can create a nested dictionary of section, subsection, … as follows:

hdgs = create_heading_dict(report); print(hdgs)

{
    '**PPMi** .... page 1': {},
    'LIST OF FIGURES .... page 5': {},
    'Abbreviations and terminology .... page 6': {},
    'Key terminology .... page 8': {},
    'Executive summary .... page 10': {'Background .... page 10': {}},
    'Methodology .... page 11': {},
    'Findings .... page 12': {'Relevance .... page 12': {}},
    'Coherence .... page 13': {'$4.3 / 5$ .... page 13': {}},
    'Effectiveness .... page 14': {
        'Specific Outcome 1: .... page 14': {},
        'Specific Outcome 2: .... page 14': {}
    },
    'Specific Outcome 3: .... page 15': {'Efficiency .... page 15': {}},
    'Sustainability .... page 16': {'Conclusions and recommendations .... page 16': {}},
    '1. Introduction .... page 18': {},
    'Part 1: Background and methodology .... page 19': {},
    '2. Background to the JI-HoA .... page 20': {'2.1. Context and design of the JI-HoA .... page 20': {}},
    '2.2. External factors affecting the implementation of the JI-HoA .... page 23': {},
    '3. Methodology of the evaluation .... page 26': {'3.1. Evaluation framework .... page 26': {}},
    'TABLE 2. INTERVENTION LOGIC .... page 27': {},
    '3.2. Evaluation matrix .... page 29': {'3.3. Data collection .... page 29': {}},
    '3.4. Scoring system .... page 30': {},
    '3.5. Limitations .... page 32': {},
    'Part 2: Findings .... page 33': {},
    '4. Relevance .... page 34': {
        'Overall performance score for relevance: 3.9/5. .... page 34': {},
        'Robustness score for the evidence: 4.5/5. .... page 34': {
            '4.1. Relevance of programme activities for migrants, returnees, and communities .... page 34': {
                '4.1.1. Needs of migrants .... page 34': {}
            }
        }
    },
    '4.1.2. Needs of returnees .... page 35': {},
    '4.1.3. Needs of community members .... page 38': {},
    "4.2. Programme's relevance to the needs of stakeholders .... page 39": {
        '4.2.1. Needs of governments .... page 39': {}
    },
    '4.2.2. Needs of other stakeholders .... page 40': {},
    '4.3. Involvement of stakeholders in the design, implementation and monitoring of the programme .... page 41': 
{},
    '4.4. Horizontal priorities .... page 42': {'4.4.1. Gender equality .... page 42': {}},
    '4.4.2. Persons with disabilities .... page 43': {'4.4.3. Protection .... page 43': {}},
    '4.4.4. Environmental sustainability .... page 44': {},
    '5. Coherence .... page 46': {
        'Overall performance score for coherence: $4.3 / 5$. .... page 46': {},
        'Robustness score for the evidence: $4 / 5$. .... page 46': {
            "5.1. The JI-HoA's alignment with the objectives and standards of IOM, and the objectives of the EU 
.... page 46": {
                '5.1.1. Objectives of the IOM .... page 46': {}
            }
        }
    },
    '5.1.2. Objectives of the EU .... page 48': {},
    '5.1.3. Government initiatives .... page 49': {},
    '5.2. Alignment with other initiatives .... page 50': {
        '5.2.1. Initiatives of regional and continental institutions .... page 50': {}
    },
    '5.2.2. Initiatives by other (UN) organisations .... page 51': {},
    '6. Effectiveness and impact .... page 54': {
        'Overall performance score for effectiveness: 3.8/5. .... page 54': {},
        "Overall score on IOM's achievements: 3.4/5. .... page 54": {},
        'Robustness score for the evidence: $4 / 5$. .... page 54': {
            "6.1. Design and achievement of targets for the programme's indicators .... page 54": {}
        }
    },
    '6.2. Specific Objective 1: partner countries and relevant stakeholders developed or strengthened 
evidence-based return and reintegration procedures .... page 57': {
        '6.2.1. Achievement of outputs and results .... page 57': {'Data availability .... page 57': {}},
        'Capacity of stakeholders .... page 58': {},
        'Capacity of the African Union Commission .... page 61': {}
    },
    '6.2.2. Achievement of Specific Objective 1 .... page 62': {},
    '6.3. Specific Objective 2: safe, humane, dignified voluntary return processes are enhanced along main 
migration routes .... page 64': {
        '6.3.1. Achievement of outputs and results .... page 64': {'Outreach and awareness .... page 64': {}},
        'Assistance to stranded migrants .... page 66': {}
    },
    '6.3.2. Achievement of Specific Objective 2 .... page 67': {
        '6.4. Specific Objective 3: returnees are sustainably integrated in host communities, and host communities 
are better able to create living standards that address drivers of migration. .... page 67': {}
    },
    '6.4.1. Achievement of outputs and results .... page 68': {
        'Individual and community-based reintegration .... page 68': {'M&E systems .... page 70': {}}
    },
    '6.4.2. Achievement of Specific Objective 3 .... page 71': {
        'Overall achievement of reintegration .... page 71': {
            '6.5. Assessing vulnerabilities .... page 77': {
                '6.5.1. Achievements and challenges in screening migrant vulnerabilities and assessing eligibility 
for support .... page 77': {}
            }
        }
    },
    '6.5.2. Contact and communication with beneficiaries .... page 79': {},
    '6.6. Functioning of the integrated approach .... page 81': {},
    '7. Efficiency .... page 83': {
        'Overall performance score for efficiency: 4.3/5. .... page 83': {},
        'Robustness score for the evidence: 3.5/5. .... page 83': {
            '7.1. Did the programme receive sufficient resources to achieve its objectives? .... page 83': {
                "7.1.1. To what extent were financial resources sufficient to meet the programme's objectives? ....
page 83": {}
            }
        }
    },
    '7.1.2. To what extent was the "top-up" funding system efficient for planning and budgeting? .... page 86': {},
    "7.1.3. To what extent were human resources sufficient to meet the programme's objectives? .... page 87": {},
    '7.1.4. To what extent were the programme activities implemented according to the initial timeline? .... page 
88': {},
    '7.2. Cost-effectiveness and efficiency of the programme .... page 89': {
        '7.2.1. How well were the resources (funds, expertise and time) converted into results? .... page 89': {},
        'Increased efficiency through partnerships and capacity building .... page 90': {}
    },
    '7.2.2. Could the programme have been implemented in a more cost-effective manner? If so, how? .... page 91': {
        'Improved efficiency of some activities .... page 91': {
            'Decreased efficiency of some activities .... page 92': {},
            'Examples of cost reduction .... page 93': {},
            'Lessons learned in relation to cost-effectiveness .... page 94': {},
            '7.2.3. To what extent did the programme make efficiency gains by relying on existing services? .... 
page 94': {}
        }
    },
    '7.2.4. To what extent did the national referral mechanisms function effectively enough to support the JI-HoA? 
.... page 97': {},
    '8. Sustainability .... page 100': {
        'Overall performance score for sustainability: 2.5/5. .... page 100': {},
        'Robustness score for the evidence: 4.5/5. .... page 100': {
            '8.1. Main achievements in terms of the technical, managerial and financial capacity of governments and
other stakeholders to continue working on return and reintegration .... page 100': {}
        }
    },
    '8.2. Main challenges in terms of the technical, managerial, and financial capacity of governments and other 
stakeholders to continue working on return and reintegration .... page 102': {},
    'Part 3: Conclusions and Recommendations .... page 104': {},
    '9. Conclusions .... page 105': {},
    '10. Recommendations .... page 107': {
        '1. Enhance efforts with national, regional and local stakeholders to build capacity and ownership (while 
continuing the provision of funding). .... page 107': {}
    },
    '3. Increase attention on building partnerships with service providers who can function without (significant) 
funding channelled by IOM. .... page 108': {},
    '5. Explore opportunities to extend the scope of support provided to returnees, with a focus on longer-term 
reintegration. .... page 109': {},
    'ANNEXES .... page 111': {},
    'Annex 1. Evaluation framework .... page 112': {},
    'TABLE 13. EVALUATION QUESTIONS FOR THE SUSTAINABILITY CRITERION .... page 122': {},
    'Annex 2. Indicators, targets and achievements .... page 125': {},
    'Annex 3. IOM performance scores and methodology .... page 131': {},
    'Relevance .... page 132': {},
    'Coherence .... page 133': {'Effectiveness .... page 133': {}},
    'Efficiency .... page 136': {'Sustainability .... page 136': {}},
    'Annex 4. Assessment of indicators .... page 137': {},
    'Annex 5. Members of the PSCs .... page 142': {}
}

source

find_section_path

 find_section_path (hdgs:dict, target_section:str)

Find the nested key path for a given section name.

	Type	Details
hdgs	dict	The nested dictionary structure
target_section	str	The section name to find
Returns	list	The nested key path for the given section name

Exported source

def find_section_path(
    hdgs: dict, # The nested dictionary structure
    target_section: str # The section name to find
) -> list: # The nested key path for the given section name
    "Find the nested key path for a given section name."
    def search_recursive(current_dict, path=[]):
        for key, value in current_dict.items():
            current_path = path + [key]
            if key == target_section:
                return current_path
            if isinstance(value, dict):
                result = search_recursive(value, current_path)
                if result:
                    return result
        return None
    
    return search_recursive(hdgs)

Then we can retrieve the subsection path (list of nested headings to reach this specific section) in this nested hdgs dict :

path = find_section_path(hdgs, "4.1.1.1 Needs of migrants .... page 10"); path

['4. Findings .... page 10',
 'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for the evidence: $4.5 / 5$',
 '4.1.1.1 Needs of migrants .... page 10']

Then retrieve the specific subsection content:

source

get_content_tool

 get_content_tool (hdgs:dict, keys_list:list)

Navigate through nested levels using the exact key strings.

	Type	Details
hdgs	dict	The nested dictionary structure
keys_list	list	The list of keys to navigate through
Returns	str	The content of the section

Exported source

def get_content_tool(
    hdgs: dict, # The nested dictionary structure
    keys_list: list, # The list of keys to navigate through
    ) -> str: # The content of the section
    "Navigate through nested levels using the exact key strings."
    return reduce(lambda current, key: current[key], keys_list, hdgs).text

content = get_content_tool(hdgs, path)
print(content[:500])

##### 4.1.1.1 Needs of migrants .... page 10

Desk research and interviews confirm that the programme responded to the most pressing needs of migrants. The 
JI-HoA enabled them to return from dangerous environments, such as detention, where no other support was available.
Migrants shared that they suffered on their irregular migration journeys, had acutely distressing experiences ${ 
}^{13}$, and highlighted that their families and communities could not help them ${ }^{14}$. Stakeholders supportin

source

flatten_sections

 flatten_sections (hdgs, path=[])

Extract flat list of (key, full_path) tuples from nested hdgs

Exported source

def flatten_sections(hdgs, path=[]):
    """Extract flat list of (key, full_path) tuples from nested hdgs"""
    sections = []
    for key, value in hdgs.items():
        current_path = path + [key]
        sections.append((key, current_path))
        if isinstance(value, dict):
            sections.extend(flatten_sections(value, current_path))
    return sections

print(flatten_sections(hdgs))

[
    ('PPMi .... page 1', ['PPMi .... page 1']),
    ('CONTENTS .... page 3', ['CONTENTS .... page 3']),
    ('1. Introduction .... page 4', ['1. Introduction .... page 4']),
    ('2. Background of the JI-HoA .... page 5', ['2. Background of the JI-HoA .... page 5']),
    (
        '2.1. Context and design of the JI-HoA .... page 5',
        ['2. Background of the JI-HoA .... page 5', '2.1. Context and design of the JI-HoA .... page 5']
    ),
    (
        '2.2. External factors affecting the implementation of the JI .... page 7',
        [
            '2. Background of the JI-HoA .... page 5',
            '2.2. External factors affecting the implementation of the JI .... page 7'
        ]
    ),
    ('3. Methodology .... page 8', ['3. Methodology .... page 8']),
    ('4. Findings .... page 10', ['4. Findings .... page 10']),
    ('4.1. Relevance .... page 10', ['4. Findings .... page 10', '4.1. Relevance .... page 10']),
    (
        '4.1.1. Relevance of programme activities for migrants, returnees, and communities .... page 10',
        [
            '4. Findings .... page 10',
            '4.1. Relevance .... page 10',
            '4.1.1. Relevance of programme activities for migrants, returnees, and communities .... page 10'
        ]
    ),
    (
        'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for the evidence: $4.5 / 5$',
        [
            '4. Findings .... page 10',
            'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for the evidence: $4.5 / 5$'
        ]
    ),
    (
        '4.1.1.1 Needs of migrants .... page 10',
        [
            '4. Findings .... page 10',
            'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for the evidence: $4.5 / 5$',
            '4.1.1.1 Needs of migrants .... page 10'
        ]
    ),
    (
        '4.1.1.2 Needs of returnees .... page 10',
        [
            '4. Findings .... page 10',
            'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for the evidence: $4.5 / 5$',
            '4.1.1.2 Needs of returnees .... page 10'
        ]
    ),
    (
        '4.1.1.3 Needs of community members .... page 12',
        [
            '4. Findings .... page 10',
            'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for the evidence: $4.5 / 5$',
            '4.1.1.3 Needs of community members .... page 12'
        ]
    ),
    (
        "4.1.2. Programme's relevance to the needs of stakeholders .... page 12",
        [
            '4. Findings .... page 10',
            'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for the evidence: $4.5 / 5$',
            "4.1.2. Programme's relevance to the needs of stakeholders .... page 12"
        ]
    ),
    (
        '4.1.2.1 Needs of governments .... page 12',
        [
            '4. Findings .... page 10',
            'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for the evidence: $4.5 / 5$',
            "4.1.2. Programme's relevance to the needs of stakeholders .... page 12",
            '4.1.2.1 Needs of governments .... page 12'
        ]
    ),
    (
        '4.1.2.2 Needs of other stakeholders .... page 13',
        [
            '4. Findings .... page 10',
            'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for the evidence: $4.5 / 5$',
            "4.1.2. Programme's relevance to the needs of stakeholders .... page 12",
            '4.1.2.2 Needs of other stakeholders .... page 13'
        ]
    ),
    (
        '4.2. Coherence .... page 13',
        [
            '4. Findings .... page 10',
            'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for the evidence: $4.5 / 5$',
            '4.2. Coherence .... page 13'
        ]
    ),
    (
        "4.2.1. The JI-HoA's alignment with the objectives and standards of IOM, and objectives of the EU .... page
14",
        [
            '4. Findings .... page 10',
            'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for the evidence: $4.5 / 5$',
            '4.2. Coherence .... page 13',
            "4.2.1. The JI-HoA's alignment with the objectives and standards of IOM, and objectives of the EU .... 
page 14"
        ]
    ),
    (
        '4.2.2. Alignment with other initiatives .... page 14',
        [
            '4. Findings .... page 10',
            'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for the evidence: $4.5 / 5$',
            '4.2. Coherence .... page 13',
            '4.2.2. Alignment with other initiatives .... page 14'
        ]
    ),
    ('4.3. Effectiveness .... page 16', ['4.3. Effectiveness .... page 16']),
    (
        '4.3.1. Specific Objective 1: Partner countries and relevant stakeholders developed or strengthened 
evidence-based return and reintegration procedures .... page 16',
        [
            '4.3. Effectiveness .... page 16',
            '4.3.1. Specific Objective 1: Partner countries and relevant stakeholders developed or strengthened 
evidence-based return and reintegration procedures .... page 16'
        ]
    ),
    (
        '4.3.1.1 Achievement of outputs and results .... page 16',
        [
            '4.3. Effectiveness .... page 16',
            '4.3.1. Specific Objective 1: Partner countries and relevant stakeholders developed or strengthened 
evidence-based return and reintegration procedures .... page 16',
            '4.3.1.1 Achievement of outputs and results .... page 16'
        ]
    ),
    ('Data availability .... page 16', ['4.3. Effectiveness .... page 16', 'Data availability .... page 16']),
    (
        '4.3.1.2 Achievement of Specific Objective 1 .... page 17',
        [
            '4.3. Effectiveness .... page 16',
            'Data availability .... page 16',
            '4.3.1.2 Achievement of Specific Objective 1 .... page 17'
        ]
    ),
    (
        '4.3.1.2 Achievement of Specific Objective 1 .... page 17',
        [
            '4.3. Effectiveness .... page 16',
            'Data availability .... page 16',
            '4.3.1.2 Achievement of Specific Objective 1 .... page 17',
            '4.3.1.2 Achievement of Specific Objective 1 .... page 17'
        ]
    ),
    (
        '4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along main 
migration routes .... page 18',
        [
            '4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along 
main migration routes .... page 18'
        ]
    ),
    (
        '4.3.2.1 Achievement of outputs and results .... page 19',
        [
            '4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along 
main migration routes .... page 18',
            '4.3.2.1 Achievement of outputs and results .... page 19'
        ]
    ),
    (
        'Outreach and awareness .... page 19',
        [
            '4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along 
main migration routes .... page 18',
            'Outreach and awareness .... page 19'
        ]
    ),
    (
        'Assistance to stranded migrants .... page 19',
        [
            '4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along 
main migration routes .... page 18',
            'Assistance to stranded migrants .... page 19'
        ]
    ),
    (
        '4.3.2.2 Achievement of the Objective .... page 20',
        [
            '4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along 
main migration routes .... page 18',
            'Assistance to stranded migrants .... page 19',
            '4.3.2.2 Achievement of the Objective .... page 20'
        ]
    ),
    (
        '4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host 
communities are better able to create living standards that address drivers of migration. .... page 20',
        [
            '4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along 
main migration routes .... page 18',
            'Assistance to stranded migrants .... page 19',
            '4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host 
communities are better able to create living standards that address drivers of migration. .... page 20'
        ]
    ),
    (
        '4.3.3.1 Achievement of outputs and results .... page 20',
        [
            '4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along 
main migration routes .... page 18',
            'Assistance to stranded migrants .... page 19',
            '4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host 
communities are better able to create living standards that address drivers of migration. .... page 20',
            '4.3.3.1 Achievement of outputs and results .... page 20'
        ]
    ),
    (
        'Individual and community-based reintegration .... page 20',
        [
            '4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along 
main migration routes .... page 18',
            'Individual and community-based reintegration .... page 20'
        ]
    ),
    (
        '4.3.3.2 Achievement of Specific Objective 3 .... page 21',
        [
            '4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along 
main migration routes .... page 18',
            'Individual and community-based reintegration .... page 20',
            '4.3.3.2 Achievement of Specific Objective 3 .... page 21'
        ]
    ),
    ('4.3.3.2 Achievement of Specific Objective 3', ['4.3.3.2 Achievement of Specific Objective 3']),
    (
        'Overall achievement of reintegration .... page 22',
        ['4.3.3.2 Achievement of Specific Objective 3', 'Overall achievement of reintegration .... page 22']
    ),
    (
        '4.3.4. Functioning of the Integrated Approach .... page 23',
        [
            '4.3.3.2 Achievement of Specific Objective 3',
            'Overall achievement of reintegration .... page 22',
            '4.3.4. Functioning of the Integrated Approach .... page 23'
        ]
    ),
    (
        '4.3.4. Functioning of the Integrated Approach .... page 23',
        [
            '4.3.3.2 Achievement of Specific Objective 3',
            'Overall achievement of reintegration .... page 22',
            '4.3.4. Functioning of the Integrated Approach .... page 23',
            '4.3.4. Functioning of the Integrated Approach .... page 23'
        ]
    ),
    ('4.4. Efficiency .... page 24', ['4.4. Efficiency .... page 24']),
    (
        '4.4.3. Did the programme receive sufficient resources to achieve its objectives? .... page 24',
        [
            '4.4. Efficiency .... page 24',
            '4.4.3. Did the programme receive sufficient resources to achieve its objectives? .... page 24'
        ]
    ),
    (
        '4.4.2. Cost-effectiveness and efficiency of the programme .... page 25',
        [
            '4.4. Efficiency .... page 24',
            '4.4.2. Cost-effectiveness and efficiency of the programme .... page 25'
        ]
    ),
    ('4.5. Sustainability .... page 26', ['4.5. Sustainability .... page 26']),
    (
        'Overall performance score for sustainability: $2.5 / 5$ <br> Robustness score for the evidence: $4 / 5$ 
.... page 26',
        [
            '4.5. Sustainability .... page 26',
            'Overall performance score for sustainability: $2.5 / 5$ <br> Robustness score for the evidence: $4 / 
5$ .... page 26'
        ]
    ),
    ('5. Conclusions and Recommendations .... page 27', ['5. Conclusions and Recommendations .... page 27']),
    (
        '5.1. Conclusions .... page 27',
        ['5. Conclusions and Recommendations .... page 27', '5.1. Conclusions .... page 27']
    ),
    (
        '5.2. Recommendations .... page 28',
        ['5. Conclusions and Recommendations .... page 27', '5.2. Recommendations .... page 28']
    ),
    (
        '5.2.1. Increase attention on building partnerships with service providers who can function without 
(significant) funding channelled by IOM. .... page 29',
        [
            '5. Conclusions and Recommendations .... page 27',
            '5.2. Recommendations .... page 28',
            '5.2.1. Increase attention on building partnerships with service providers who can function without 
(significant) funding channelled by IOM. .... page 29'
        ]
    ),
    (
        '5.2.2. Explore opportunities to extend the scope of support provided to returnees, with a focus on 
longer-term integration. .... page 30',
        [
            '5. Conclusions and Recommendations .... page 27',
            '5.2. Recommendations .... page 28',
            '5.2.2. Explore opportunities to extend the scope of support provided to returnees, with a focus on 
longer-term integration. .... page 30'
        ]
    )
]

source

extract_content

 extract_content (section_key:str, sections_lookup:dict, hdgs:dict)

Exported source

def extract_content(section_key: str, sections_lookup: dict, hdgs: dict) -> str:
    path = sections_lookup[section_key]
    return get_content_tool(hdgs, path)

source

format_sections_for_selection

 format_sections_for_selection (available_sections:List[str])

Format available sections as indexed JSON array

Exported source

def format_sections_for_selection(available_sections: List[str]) -> str:
    "Format available sections as indexed JSON array"
    return json.dumps([
        [i+1, s] for i, s in enumerate(available_sections)
    ], indent=2)

print(report[:500])

# PPMi .... page 1

**Final Evaluation of the EU-IOM Joint Initiative for migrant protection and reintegration in the horn of Africa**

Final Evaluation Report, 17 March 2023

!(img-0.jpeg)

**EU-IOM** Joint Initiative for Migrant Protection and Reintegration

Project funded by the European Union
Project implemented by IOM

---

This Final Evaluation Report was commissioned by IOM and developed by the evaluation team of PPMI Group, including:
Loes van der Graaf, Rimantas Dumcius, Rad

sections_lookup = {key: path for key, path in flatten_sections(hdgs)}

for k,v in sections_lookup.items():
    print(f'Section name: {k}\nPath: {v}')

Section name: PPMi .... page 1
Path: ['PPMi .... page 1']

Section name: CONTENTS .... page 3
Path: ['CONTENTS .... page 3']

Section name: 1. Introduction .... page 4
Path: ['1. Introduction .... page 4']

Section name: 2. Background of the JI-HoA .... page 5
Path: ['2. Background of the JI-HoA .... page 5']

Section name: 2.1. Context and design of the JI-HoA .... page 5
Path: ['2. Background of the JI-HoA .... page 5', '2.1. Context and design of the JI-HoA .... page 5']

Section name: 2.2. External factors affecting the implementation of the JI .... page 7
Path: ['2. Background of the JI-HoA .... page 5', '2.2. External factors affecting the implementation of the JI 
.... page 7']

Section name: 3. Methodology .... page 8
Path: ['3. Methodology .... page 8']

Section name: 4. Findings .... page 10
Path: ['4. Findings .... page 10']

Section name: 4.1. Relevance .... page 10
Path: ['4. Findings .... page 10', '4.1. Relevance .... page 10']

Section name: 4.1.1. Relevance of programme activities for migrants, returnees, and communities .... page 10
Path: ['4. Findings .... page 10', '4.1. Relevance .... page 10', '4.1.1. Relevance of programme activities for 
migrants, returnees, and communities .... page 10']

Section name: Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for the evidence: $4.5 / 5$
Path: ['4. Findings .... page 10', 'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for 
the evidence: $4.5 / 5$']

Section name: 4.1.1.1 Needs of migrants .... page 10
Path: ['4. Findings .... page 10', 'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for 
the evidence: $4.5 / 5$', '4.1.1.1 Needs of migrants .... page 10']

Section name: 4.1.1.2 Needs of returnees .... page 10
Path: ['4. Findings .... page 10', 'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for 
the evidence: $4.5 / 5$', '4.1.1.2 Needs of returnees .... page 10']

Section name: 4.1.1.3 Needs of community members .... page 12
Path: ['4. Findings .... page 10', 'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for 
the evidence: $4.5 / 5$', '4.1.1.3 Needs of community members .... page 12']

Section name: 4.1.2. Programme's relevance to the needs of stakeholders .... page 12
Path: ['4. Findings .... page 10', 'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for 
the evidence: $4.5 / 5$', "4.1.2. Programme's relevance to the needs of stakeholders .... page 12"]

Section name: 4.1.2.1 Needs of governments .... page 12
Path: ['4. Findings .... page 10', 'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for 
the evidence: $4.5 / 5$', "4.1.2. Programme's relevance to the needs of stakeholders .... page 12", '4.1.2.1 Needs 
of governments .... page 12']

Section name: 4.1.2.2 Needs of other stakeholders .... page 13
Path: ['4. Findings .... page 10', 'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for 
the evidence: $4.5 / 5$', "4.1.2. Programme's relevance to the needs of stakeholders .... page 12", '4.1.2.2 Needs 
of other stakeholders .... page 13']

Section name: 4.2. Coherence .... page 13
Path: ['4. Findings .... page 10', 'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for 
the evidence: $4.5 / 5$', '4.2. Coherence .... page 13']

Section name: 4.2.1. The JI-HoA's alignment with the objectives and standards of IOM, and objectives of the EU ....
page 14
Path: ['4. Findings .... page 10', 'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for 
the evidence: $4.5 / 5$', '4.2. Coherence .... page 13', "4.2.1. The JI-HoA's alignment with the objectives and 
standards of IOM, and objectives of the EU .... page 14"]

Section name: 4.2.2. Alignment with other initiatives .... page 14
Path: ['4. Findings .... page 10', 'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for 
the evidence: $4.5 / 5$', '4.2. Coherence .... page 13', '4.2.2. Alignment with other initiatives .... page 14']

Section name: 4.3. Effectiveness .... page 16
Path: ['4.3. Effectiveness .... page 16']

Section name: 4.3.1. Specific Objective 1: Partner countries and relevant stakeholders developed or strengthened 
evidence-based return and reintegration procedures .... page 16
Path: ['4.3. Effectiveness .... page 16', '4.3.1. Specific Objective 1: Partner countries and relevant stakeholders
developed or strengthened evidence-based return and reintegration procedures .... page 16']

Section name: 4.3.1.1 Achievement of outputs and results .... page 16
Path: ['4.3. Effectiveness .... page 16', '4.3.1. Specific Objective 1: Partner countries and relevant stakeholders
developed or strengthened evidence-based return and reintegration procedures .... page 16', '4.3.1.1 Achievement of
outputs and results .... page 16']

Section name: Data availability .... page 16
Path: ['4.3. Effectiveness .... page 16', 'Data availability .... page 16']

Section name: 4.3.1.2 Achievement of Specific Objective 1 .... page 17
Path: ['4.3. Effectiveness .... page 16', 'Data availability .... page 16', '4.3.1.2 Achievement of Specific 
Objective 1 .... page 17', '4.3.1.2 Achievement of Specific Objective 1 .... page 17']

Section name: 4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along 
main migration routes .... page 18
Path: ['4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along main 
migration routes .... page 18']

Section name: 4.3.2.1 Achievement of outputs and results .... page 19
Path: ['4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along main 
migration routes .... page 18', '4.3.2.1 Achievement of outputs and results .... page 19']

Section name: Outreach and awareness .... page 19
Path: ['4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along main 
migration routes .... page 18', 'Outreach and awareness .... page 19']

Section name: Assistance to stranded migrants .... page 19
Path: ['4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along main 
migration routes .... page 18', 'Assistance to stranded migrants .... page 19']

Section name: 4.3.2.2 Achievement of the Objective .... page 20
Path: ['4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along main 
migration routes .... page 18', 'Assistance to stranded migrants .... page 19', '4.3.2.2 Achievement of the 
Objective .... page 20']

Section name: 4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host 
communities are better able to create living standards that address drivers of migration. .... page 20
Path: ['4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along main 
migration routes .... page 18', 'Assistance to stranded migrants .... page 19', '4.3.3. Specific Objective 3: 
Returnees are sustainably integrated in host communities, and host communities are better able to create living 
standards that address drivers of migration. .... page 20']

Section name: 4.3.3.1 Achievement of outputs and results .... page 20
Path: ['4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along main 
migration routes .... page 18', 'Assistance to stranded migrants .... page 19', '4.3.3. Specific Objective 3: 
Returnees are sustainably integrated in host communities, and host communities are better able to create living 
standards that address drivers of migration. .... page 20', '4.3.3.1 Achievement of outputs and results .... page 
20']

Section name: Individual and community-based reintegration .... page 20
Path: ['4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along main 
migration routes .... page 18', 'Individual and community-based reintegration .... page 20']

Section name: 4.3.3.2 Achievement of Specific Objective 3 .... page 21
Path: ['4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along main 
migration routes .... page 18', 'Individual and community-based reintegration .... page 20', '4.3.3.2 Achievement 
of Specific Objective 3 .... page 21']

Section name: 4.3.3.2 Achievement of Specific Objective 3
Path: ['4.3.3.2 Achievement of Specific Objective 3']

Section name: Overall achievement of reintegration .... page 22
Path: ['4.3.3.2 Achievement of Specific Objective 3', 'Overall achievement of reintegration .... page 22']

Section name: 4.3.4. Functioning of the Integrated Approach .... page 23
Path: ['4.3.3.2 Achievement of Specific Objective 3', 'Overall achievement of reintegration .... page 22', '4.3.4. 
Functioning of the Integrated Approach .... page 23', '4.3.4. Functioning of the Integrated Approach .... page 23']

Section name: 4.4. Efficiency .... page 24
Path: ['4.4. Efficiency .... page 24']

Section name: 4.4.3. Did the programme receive sufficient resources to achieve its objectives? .... page 24
Path: ['4.4. Efficiency .... page 24', '4.4.3. Did the programme receive sufficient resources to achieve its 
objectives? .... page 24']

Section name: 4.4.2. Cost-effectiveness and efficiency of the programme .... page 25
Path: ['4.4. Efficiency .... page 24', '4.4.2. Cost-effectiveness and efficiency of the programme .... page 25']

Section name: 4.5. Sustainability .... page 26
Path: ['4.5. Sustainability .... page 26']

Section name: Overall performance score for sustainability: $2.5 / 5$ <br> Robustness score for the evidence: $4 / 
5$ .... page 26
Path: ['4.5. Sustainability .... page 26', 'Overall performance score for sustainability: $2.5 / 5$ <br> Robustness
score for the evidence: $4 / 5$ .... page 26']

Section name: 5. Conclusions and Recommendations .... page 27
Path: ['5. Conclusions and Recommendations .... page 27']

Section name: 5.1. Conclusions .... page 27
Path: ['5. Conclusions and Recommendations .... page 27', '5.1. Conclusions .... page 27']

Section name: 5.2. Recommendations .... page 28
Path: ['5. Conclusions and Recommendations .... page 27', '5.2. Recommendations .... page 28']

Section name: 5.2.1. Increase attention on building partnerships with service providers who can function without 
(significant) funding channelled by IOM. .... page 29
Path: ['5. Conclusions and Recommendations .... page 27', '5.2. Recommendations .... page 28', '5.2.1. Increase 
attention on building partnerships with service providers who can function without (significant) funding channelled
by IOM. .... page 29']

Section name: 5.2.2. Explore opportunities to extend the scope of support provided to returnees, with a focus on 
longer-term integration. .... page 30
Path: ['5. Conclusions and Recommendations .... page 27', '5.2. Recommendations .... page 28', '5.2.2. Explore 
opportunities to extend the scope of support provided to returnees, with a focus on longer-term integration. .... 
page 30']

Formatters

We define here a set of function formatting both evaluation frameworks themes to analyze (SRF enablers, objectives, GCM objectives, …) and traces.

source

format_enabler_theme

 format_enabler_theme (theme:evaluatr.frameworks.EvalData)

Format SRF enabler into structured text for LM processing.

	Type	Details
theme	EvalData	The theme object
Returns	str	The formatted theme string

Exported source

def format_enabler_theme(
    theme: EvalData # The theme object
    ) -> str: # The formatted theme string
    "Format SRF enabler into structured text for LM processing."
    parts = [
        f'## Enabler {theme.id}: {theme.title}',
        '### Description', 
        theme.description
    ]
    return '\n'.join(parts)

For instance:

eval_data = IOMEvalData()
data_evidence = eval_data.srf_enablers[3]  # "Data and evidence" is at index 3
print(format_enabler_theme(data_evidence))

## Enabler 4: Data and evidence
### Description
IOM will be the pre-eminent source of migration and displacement data for action, which help save lives and deliver
solutions; data for insight, which help facilitate regular migration pathways; and data for foresight, which help 
drive anticipatory action. IOM will have the systems and data fluency to collect, safely store, analyze, share and 
apply disaggregated data and evidence across the mobility spectrum. Our extensive data and research repositories 
will underpin evidence-based policies and practices. Data will be central to the internal decision-making and 
management of the Organization.

source

format_crosscutting_theme

 format_crosscutting_theme (theme:evaluatr.frameworks.EvalData)

Format SRF cross-cutting into structured text for LM processing.

	Type	Details
theme	EvalData	The theme object
Returns	str	The formatted theme string

Exported source

def format_crosscutting_theme(
    theme: EvalData # The theme object
    ) -> str: # The formatted theme string
    "Format SRF cross-cutting into structured text for LM processing."
    parts = [
        f'## Cross-cutting {theme.id}: {theme.title}',
        '### Description', 
        theme.description
    ]
    return '\n'.join(parts)

For instance:

eval_data = IOMEvalData()
env_sustainability = eval_data.srf_crosscutting_priorities[3]  # "Data and evidence" is at index 3
print(format_crosscutting_theme(env_sustainability))

## Cross-cutting 4: Environmental Sustainability
### Description
IOM will lead environmental sustainability innovation for impact and scale in the humanitarian and migration 
management sector. Caring for people and the planet is one of our core values, and we are committed to 
mainstreaming environmental sustainability into our projects and programmes, and facilities management and 
operations. IOM will have an ambitious environmental governance and environmental management system drawing from 
United Nations system-wide commitments

source

format_gcm_theme

 format_gcm_theme (theme:dict)

Format GCM objective into structured text for LM processing.

	Type	Details
theme	dict	The GCM theme object from gcm_small
Returns	str	The formatted theme string

Exported source

def format_gcm_theme(
    theme: dict # The GCM theme object from gcm_small
    ) -> str: # The formatted theme string
    "Format GCM objective into structured text for LM processing."
    parts = [
        f'## GCM Objective {theme["id"]}: {theme["title"]}',
        '### Core Theme', 
        theme["core_theme"]
    ]
    
    if theme.get("key_principles"):
        parts.extend(['### Key Principles', ', '.join(theme["key_principles"])])
    
    if theme.get("target_groups"):
        parts.extend(['### Target Groups', ', '.join(theme["target_groups"])])
        
    if theme.get("main_activities"):
        parts.extend(['### Main Activities', ', '.join(theme["main_activities"])])
    
    return '\n'.join(parts)

For instance:

gcm_small = eval_data.gcm_objectives_small
print(format_gcm_theme(gcm_small[0]))

## GCM Objective 1: Collect and utilize accurate and disaggregated data as a basis for evidence-based policies
### Core Theme
Strengthen global evidence base on migration through improved data collection, analysis and dissemination
### Key Principles
Evidence-based policymaking, Data harmonization, Statistical standards, Privacy protection
### Target Groups
National statistical offices, Researchers, Policymakers, International organizations
### Main Activities
Data collection methodologies, Migration statistics, Research capacity building, Data sharing platforms

source

format_srf_output

 format_srf_output (output_context:dict)

Format SRF output with full hierarchical context for LM processing.

Exported source

def format_srf_output(output_context: dict) -> str:
    "Format SRF output with full hierarchical context for LM processing."
    parts = [
        f'## SRF Output {output_context["output"]["id"]}: {output_context["output"]["title"]}',
        '### Strategic Context',
        f'**Objective {output_context["objective"]["id"]}**: {output_context["objective"]["title"]}',
        f'**Long    -term Outcome {output_context["long_outcome"]["id"]}**: {output_context["long_outcome"]["title"]}',
        f'**Short-term Outcome {output_context["short_outcome"]["id"]}**: {output_context["short_outcome"]["title"]}'
    ]
    
    return '\n'.join(parts)

For instance:

test_output_id = '1a11'
output_context = find_srf_output_by_id(eval_data, test_output_id)
if output_context:
    formatted = format_srf_output(output_context)
    print(formatted)

## SRF Output 1a11: Crisis-affected populations in-need receive dignified shelter and settlement support.
### Strategic Context
**Objective 1**: Saving lives and protecting people on the move
**Long    -term Outcome 1a**: Human suffering is alleviated while the dignity and rights of people affected by 
crises are upheld.
**Short-term Outcome 1a1**: Crisis-affected populations have their basic needs met and have minimum living 
conditions with reduced barriers to access for marginalized and vulnerable individuals.

Pydantic models

source

SelectSectionOutput

 SelectSectionOutput (section_index:int, reasoning:str)

Select the next most relevant section based on current evidence summary and gaps

Exported source

class SelectSectionOutput(BaseModel):
    "Select the next most relevant section based on current evidence summary and gaps"
    section_index: int
    reasoning: str

source

SelectSectionOutput

 SelectSectionOutput (section_index:int, reasoning:str)

Select the next most relevant section based on current evidence summary and gaps

Exported source

class SelectSectionOutput(BaseModel):
    "Select the next most relevant section based on current evidence summary and gaps"
    section_index: int
    reasoning: str

source

SummarizeContentOutput

 SummarizeContentOutput (summary:str, key_findings:List[str])

Summarize the content of a section and identify the key findings

Exported source

class SummarizeContentOutput(BaseModel):
    "Summarize the content of a section and identify the key findings"
    summary: str
    key_findings: List[str]

source

EvaluateEvidenceOutput

 EvaluateEvidenceOutput (theme_covered:bool, coverage_reasoning:str,
                         gaps_identified:str, should_continue:bool)

*!!! abstract “Usage Documentation” Models

A base class for creating Pydantic models.

Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
__pydantic_core_schema__: The core schema of the model.
__pydantic_custom_init__: Whether the model has a custom `__init__` function.
__pydantic_decorators__: Metadata containing the decorators defined on the model.
    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
__pydantic_post_init__: The name of the post-init method for the model, if defined.
__pydantic_root_model__: Whether the model is a [`RootModel`][pydantic.root_model.RootModel].
__pydantic_serializer__: The `pydantic-core` `SchemaSerializer` used to dump instances of the model.
__pydantic_validator__: The `pydantic-core` `SchemaValidator` used to validate instances of the model.

__pydantic_fields__: A dictionary of field names and their corresponding [`FieldInfo`][pydantic.fields.FieldInfo] objects.
__pydantic_computed_fields__: A dictionary of computed field names and their corresponding [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_extra__: A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra]
    is set to `'allow'`.
__pydantic_fields_set__: The names of fields explicitly set during instantiation.
__pydantic_private__: Values of private attributes set on the model instance.*

Exported source

class EvaluateEvidenceOutput(BaseModel):
    theme_covered: bool
    coverage_reasoning: str
    gaps_identified: str
    should_continue: bool

source

State

 State (theme:str, prior_coverage_context:str='',
        section_summaries:List[dict]=[], explored_sections:List[str]=[],
        available_sections:List[str], evaluation_history:List[dict]=[],
        iterations_completed:int=0, theme_covered:bool=False,
        coverage_reasoning:str='', gaps_identified:str='',
        stop_reason:str='')

*!!! abstract “Usage Documentation” Models

A base class for creating Pydantic models.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
__pydantic_core_schema__: The core schema of the model.
__pydantic_custom_init__: Whether the model has a custom `__init__` function.
__pydantic_decorators__: Metadata containing the decorators defined on the model.
    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
__pydantic_post_init__: The name of the post-init method for the model, if defined.
__pydantic_root_model__: Whether the model is a [`RootModel`][pydantic.root_model.RootModel].
__pydantic_serializer__: The `pydantic-core` `SchemaSerializer` used to dump instances of the model.
__pydantic_validator__: The `pydantic-core` `SchemaValidator` used to validate instances of the model.

__pydantic_fields__: A dictionary of field names and their corresponding [`FieldInfo`][pydantic.fields.FieldInfo] objects.
__pydantic_computed_fields__: A dictionary of computed field names and their corresponding [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_extra__: A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra]
    is set to `'allow'`.
__pydantic_fields_set__: The names of fields explicitly set during instantiation.
__pydantic_private__: Values of private attributes set on the model instance.*

Exported source

class State(BaseModel):
    theme: str
    prior_coverage_context: str = ""
    section_summaries: List[dict] = []  # Renamed from evidences
    explored_sections: List[str] = []
    available_sections: List[str]
    evaluation_history: List[dict] = []  # Track reasoning evolution
    iterations_completed: int = 0
    theme_covered: bool = False
    coverage_reasoning: str = ""
    gaps_identified: str = ""
    stop_reason: str = ""

System prompts

Exported source

select_section_sp = """### ROLE AND OBJECTIVE
You are a strategic document navigator. Your job is to select the next most relevant section to explore for evidence about a specific theme.

### CONTEXT
You will receive:
- The theme being analyzed
- Current evidence summary and identified gaps
- Available sections (as JSON array of [index, section_name] pairs)
- Sections already explored

### TASK INSTRUCTIONS
Select the next section most likely to contain NEW, relevant evidence about this theme.

### SELECTION RULES
1. Choose sections that directly address the theme (not tangentially related)
2. NEVER select a subsection if its parent section was already explored
   - Example: If "4. Findings" explored → skip "4.1 Relevance", "4.1.1 Needs", etc.
3. Prioritize sections that address gaps identified in previous evaluations
4. Select "DONE" only when no unexplored sections remain OR all remaining sections are clearly irrelevant

### EXAMPLES
**Example 1: Parent → Child is FORBIDDEN**
Explored: ["4. Findings"]
Available: ["4.1 Relevance", "4.2 Coherence", "5. Conclusions"]
✓ Valid: "5. Conclusions" (independent section)
✗ Invalid: "4.1 Relevance", "4.2 Coherence" (children of explored parent "4. Findings")

**Example 2: Child → Parent is ALLOWED**
Explored: ["4.1.1 Needs of migrants"]
Available: ["4.1 Relevance", "4. Findings", "5. Conclusions"]
✓ Valid: All three are valid (parent can add context after exploring child)

**Example 3: Siblings are always OK**
Explored: ["4.1 Relevance"]
Available: ["4.2 Coherence", "4.3 Effectiveness"]
✓ Valid: Both are OK (siblings are independent)

### OUTPUT FORMAT
JSON with:
- section_index: integer from the provided pairs, or "DONE"
- reasoning: explain why this section addresses current gaps and is not a subsection of explored content
"""

Exported source

summarize_sp = """You are summarizing content from an evaluation report section.

Your task: Extract and condense the key points relevant to the theme being analyzed.

This summary will be used to:
- Maintain context across iterations without inflating the prompt
- Provide the evaluation step with essential information from this section

Keep it concise but capture:
- Main findings or claims related to the theme
- Supporting evidence (data, quotes, examples)
- Methodological details if relevant

Output JSON with:
- summary: concise summary of the content
- key_findings: list of specific findings relevant to the theme
"""

Exported source

evaluate_evidence_sp = """### ROLE AND OBJECTIVE
You are a senior UN evaluation expert. Your task: Identify what's MISSING from this report that would prevent you from writing a substantive briefing on this theme.

### YOUR TASK
Assume you need to write a 2-page briefing for leadership on this theme covering:
- What the program achieved (or didn't achieve)
- Why it succeeded or failed  
- What lessons emerged
- Evidence-based recommendations for future programs

**Start by identifying gaps**: What critical information is missing?

### CRITICAL GAPS TO CHECK
For each, ask: "Is this gap present?"

1. **Causal understanding gap**: Can't explain WHY outcomes occurred?
2. **Outcome evidence gap**: Have activities but no results/impact data?
3. **Context gap**: Missing information on challenges/barriers faced?
4. **Specificity gap**: Only vague statements, no concrete examples/numbers?
5. **Balance gap**: Only successes OR only failures, not both?

### DECISION RULE
**If ANY critical gap exists → theme_covered=False**

Only mark theme_covered=True when:
- All 5 critical gaps have been ruled out
- You have enough evidence to write a substantive 2-page briefing
- A senior evaluator would find your briefing credible and actionable

When in doubt → mark as False

### OUTPUT FORMAT
JSON with:
- theme_covered: boolean
- coverage_reasoning: Start with gaps identified, then explain if evidence overcomes them
- gaps_identified: List specific critical gaps that remain
- should_continue: boolean
"""

Theme analysis core steps

source

parse_response

 parse_response (result)

Extract JSON from Lisette response

Exported source

def parse_response(result):
    "Extract JSON from Lisette response"
    return json.loads(result.choices[0].message.content)

source

format_sections_for_selection

 format_sections_for_selection (available_sections:List[str])

Format available sections as indexed JSON array

Exported source

def format_sections_for_selection(available_sections: List[str]) -> str:
    "Format available sections as indexed JSON array"
    return json.dumps([
        [i+1, s] for i, s in enumerate(available_sections)
    ], indent=2)

source

select_section

 select_section (state:__main__.State,
                 model:str='gemini/gemini-2.0-flash')

Select next section to explore based on current state

Exported source

async def select_section(
    state: State,
    model: str = 'gemini/gemini-2.0-flash'
) -> dict:
    "Select next section to explore based on current state"
    chat = AsyncChat(model=model, sp=select_section_sp, temp=0)
    
    sections_json = format_sections_for_selection(state.available_sections)
    
    # Format evaluation history for context
    eval_summary = "\n".join([
        f"Iteration {ev['iteration']}: Theme covered={ev['theme_covered']}, Gaps: {ev['gaps_identified']}"
        for ev in state.evaluation_history
    ]) if state.evaluation_history else "No evaluations yet - initial exploration"
    
    parts = [
        state.prior_coverage_context,
        f"Theme being analyzed:\n{state.theme}",
        f"Evaluation history:\n{eval_summary}",
        f"Available sections:\n{sections_json}",
        f"Explored sections: {state.explored_sections}"
    ]
    prompt = "\n\n".join(p for p in parts if p)
    
    result = await chat(prompt, response_format=SelectSectionOutput)
    parsed = parse_response(result)
    
    section_key = state.available_sections[parsed['section_index'] - 1]
    return {'section_key': section_key, 'reasoning': parsed['reasoning']}

To give an example, we first create a state with some sections:

state = State(
    theme="## Data and Evidence\nOrganizations need robust data systems...",
    available_sections=["4.1. Relevance", "4.3.1. Data availability"],
    explored_sections=["4.2. Coherence"],
    section_summaries=[{
        'summary': 'The programme aligned with IOM standards and EU objectives',
        'key_findings': ['Strong coherence with partners', 'No duplication found']
    }],
    evaluation_history=[{
        'iteration': 1,
        'theme_covered': False,
        'coverage_reasoning': 'Found alignment info but no data systems details',
        'gaps_identified': 'Need evidence on data collection, storage, and analysis'
    }]
)

Then we select the next section to explore:

result = await select_section(state)
print(f"Selected: {result['section_key']}")
print(f"Reasoning: {result['reasoning']}")

Selected: 4.3.1. Data availability

Reasoning: This section directly addresses the theme of data and evidence, and it is not a subsection of any 
explored section. It should provide evidence on data availability, which is related to data collection, storage, 
and analysis.

To prevent context bloat across iterations, we’ll store only summaries and key findings rather than full section contents in the state. The following function handles this summarization:

source

summarize_content

 summarize_content (state:__main__.State, section_key:str, content:str,
                    model:str='gemini/gemini-2.5-flash')

Summarize section content relevant to the theme

	Type	Default	Details
state	State		The current state of the analysis
section_key	str		The key of the section to summarize
content	str		The content of the section to summarize
model	str	gemini/gemini-2.5-flash	model: str = ‘gemini/gemini-2.0-flash’ # The model to use model: str = ‘claude-sonnet-4-20250514’
Returns	dict

Exported source

async def summarize_content(
    state: State, # The current state of the analysis
    section_key: str, # The key of the section to summarize
    content: str, # The content of the section to summarize
    # model: str = 'gemini/gemini-2.0-flash' # The model to use
    # model: str = 'claude-sonnet-4-20250514'
    model: str = 'gemini/gemini-2.5-flash'
) -> dict:
    "Summarize section content relevant to the theme"
    chat = AsyncChat(model=model, sp=summarize_sp, temp=0)
    
    parts = [
        state.prior_coverage_context,
        f"Theme: {state.theme}",
        f"Section: {section_key}",
        f"Content to summarize:\n{content}"
    ]
    prompt = "\n\n".join(p for p in parts if p)
    
    result = await chat(prompt, response_format=SummarizeContentOutput)
    return parse_response(result)

Assuming we have a state and selected section:

state = State(
    theme="## Data and Evidence\nOrganizations need robust data systems...",
    available_sections=["4.1. Relevance"],
    explored_sections=["4.3.1. Data availability"]
)

We can summarize the content of the section:

section_key = "4.3.1. Data availability"
content = """The JI-HoA faced significant gaps in migration data. 
The Regional Data Hub produced 20 research outputs and engaged with National Statistical Offices.
Data collection methodologies were harmonized across countries.
However, stakeholders noted that additional steps still need to be taken to improve data gathering capacities."""

result = await summarize_content(state, section_key, content)
print(f"Summary: {result['summary']}")
print(f"Key findings: {result['key_findings']}")

Summary: The JI-HoA experienced significant gaps in migration data, prompting the Regional Data Hub to produce 20 
research outputs and engage with National Statistical Offices to harmonize data collection methodologies across 
countries. Despite these efforts, stakeholders indicate that further improvements in data gathering capacities are 
still required.

Key findings: ['Significant gaps in migration data were identified within the JI-HoA.', 'The Regional Data Hub 
produced 20 research outputs and engaged with National Statistical Offices to address data deficiencies.', 'Data 
collection methodologies were harmonized across participating countries.', 'Stakeholders believe additional steps 
are necessary to enhance data gathering capacities.']

source

evaluate_evidence

 evaluate_evidence (state:__main__.State, new_content:str,
                    model:str='gemini/gemini-2.5-flash')

Evaluate evidence collected and determine if more exploration needed

	Type	Default	Details
state	State
new_content	str
model	str	gemini/gemini-2.5-flash	model: str = ‘gemini/gemini-2.0-flash’
Returns	dict		model: str = ‘claude-sonnet-4-20250514’

Exported source

async def evaluate_evidence(
    state: State,
    new_content: str,
    # model: str = 'gemini/gemini-2.0-flash'
    model: str = 'gemini/gemini-2.5-flash'
    # model: str = 'claude-sonnet-4-20250514'
) -> dict:
    "Evaluate evidence collected and determine if more exploration needed"
    chat = AsyncChat(model=model, sp=evaluate_evidence_sp, temp=0)
    
    # Format previous summaries
    prev_summaries = "\n\n".join([
        f"Section: {state.explored_sections[i]}\n"
        f"Summary: {state.section_summaries[i]['summary']}\n"
        f"Key findings: {', '.join(state.section_summaries[i]['key_findings'])}"
        for i in range(len(state.section_summaries))
    ]) if state.section_summaries else "None yet"
    
    # Format evaluation history
    prev_evaluations = "\n\n".join([
        f"Iteration {ev['iteration']}:\n"
        f"Theme covered: {ev['theme_covered']}\n"
        f"Reasoning: {ev['coverage_reasoning']}\n"
        f"Gaps: {ev['gaps_identified']}"
        for ev in state.evaluation_history
    ]) if state.evaluation_history else "First evaluation - no previous assessments"
    
    parts = [
        state.prior_coverage_context,
        f"Theme being analyzed:\n{state.theme}",
        f"Previous evidence summaries:\n{prev_summaries}",
        f"Previous evaluation reasoning:\n{prev_evaluations}",
        f"Exploration progress: {len(state.explored_sections)} sections explored out of {len(state.explored_sections) + len(state.available_sections)} total available",
        f"New content to evaluate:\n{new_content}"
    ]
    prompt = "\n\n".join(p for p in parts if p)
    
    result = await chat(prompt, response_format=EvaluateEvidenceOutput)
    return parse_response(result)

For example, let’s consider a state after exploring one section:

# #| eval: false
state = State(
    theme="## Data and Evidence\nOrganizations need robust data systems...",
    explored_sections=["4.3.1. Data availability"],
    section_summaries=[{
        'summary': 'Regional Data Hub produced 20 research outputs and engaged with National Statistical Offices',
        'key_findings': ['Data collection harmonized across countries', 'NSO engagement strengthened']
    }],
    available_sections=["4.1. Relevance", "5. Conclusions"],
    evaluation_history=[{
        'iteration': 1,
        'theme_covered': False,
        'coverage_reasoning': 'Found evidence of data production but missing details on storage systems and internal processes',
        'gaps_identified': 'Need information on data storage, security, and internal decision-making processes'
    }]
)

new_content = """The programme established data governance frameworks in Ethiopia, Somalia, and Sudan. 
National stakeholders received training on data management protocols and reporting standards. 
However, political instability and staff turnover undermined capacity building efforts. 
Survey results showed 97% of stakeholders reported increased knowledge on return and reintegration issues."""

result = await evaluate_evidence(state, new_content)
print(f"Theme covered: {result['theme_covered']}")
print(f"Coverage reasoning: {result['coverage_reasoning']}")
print(f"Should continue: {result['should_continue']}")

Theme covered: False

Coverage reasoning: The theme 'Data and Evidence' is not sufficiently covered to write a substantive 2-page 
briefing. While there is evidence of activities (Regional Data Hub, NSO engagement, governance frameworks, 
training) and some immediate outputs/outcomes (20 research outputs, harmonized data collection, 97% increased 
knowledge), critical gaps remain. We lack a clear causal understanding of *why* certain successes occurred beyond 
the program's direct actions, and the *impact* of these data systems on decision-making or program effectiveness is
largely absent. Many statements are still vague, lacking concrete details on the quality, implementation, or 
specific benefits of the achievements. Crucially, the previous gaps regarding data storage, security, and how data 
informs internal decision-making processes remain unaddressed. Without this information, recommendations would be 
superficial and not evidence-based.

Should continue: True

# Test cases for evaluate_evidence_sp
test_cases = [
    {
        "name": "Strong coverage - all gaps addressed",
        "theme": "## Data and Evidence\nOrganizations need robust data systems...",
        "content": """The programme collected migration data from 5,000 returnees across 4 countries (quantitative). 
        Stakeholders stated: "Data quality improved dramatically" (qualitative).
        Data led to 15% increase in evidence-based policies adopted (outcome).
        However, staff turnover undermined sustainability in Sudan (critical analysis).
        Evidence from Section 4.1 and Section 5.2 (multiple sources).""",
        "expected": True
    },
    {
        "name": "Missing outcome gap",
        "theme": "## Data and Evidence\nOrganizations need robust data systems...",
        "content": """We conducted 20 training sessions on data collection.
        Stakeholders were satisfied with the training.
        No data on whether this led to improved policies.""",
        "expected": False
    },
    {
        "name": "Only activities, no results",
        "theme": "## Partnership\nBuilding partnerships with stakeholders...",
        "content": """We held 15 partnership meetings.
        Signed 3 MOUs with local organizations.
        Partners attended capacity building workshops.""",
        "expected": False
    }
]

# Run a single test case
async def run_test(test_case):
    state = State(
        theme=test_case["theme"],
        section_summaries=[],
        explored_sections=[],
        available_sections=[]
    )
    
    result = await evaluate_evidence(state, test_case["content"])
    
    passed = result['theme_covered'] == test_case['expected']
    print(f"Test: {test_case['name']}")
    print(f"Expected: {test_case['expected']}, Got: {result['theme_covered']}")
    print(f"Status: {'✓ PASS' if passed else '✗ FAIL'}")
    print(f"Reasoning: {result['coverage_reasoning'][:200]}...")
    print("-" * 80)
    
# Run all tests
for test in test_cases:
    await run_test(test)

Test: Strong coverage - all gaps addressed

Expected: True, Got: True

Status: ✓ PASS

Reasoning: The provided content addresses all critical gaps. It clearly states what was achieved (data collection, 
improved quality, 15% increase in evidence-based policies) and a specific area where sustainabil...

--------------------------------------------------------------------------------

Test: Missing outcome gap

Expected: False, Got: False

Status: ✓ PASS

Reasoning: The provided content is insufficient to write a substantive 2-page briefing. While it mentions an 
activity (training sessions) and a satisfaction metric, there is an explicit lack of data on whether t...

--------------------------------------------------------------------------------

Test: Only activities, no results

Expected: False, Got: False

Status: ✓ PASS

Reasoning: The provided content only lists activities (meetings held, MOUs signed, workshops attended) related to 
partnerships. It completely lacks information on the outcomes or impact of these activities, why ...

--------------------------------------------------------------------------------

source

limit

 limit (semaphore, coro, delay=None)

Execute coroutine with semaphore concurrency control

Exported source

async def limit(semaphore, coro, delay=None):
    "Execute coroutine with semaphore concurrency control"
    async with semaphore:
        result = await coro
        if delay: await sleep(delay)
        return result

For instance, let’s create

a semaphore limiting to 3 concurrent calls:

sem = Semaphore(3)

5 different themes to analyze:

themes = [
    "## Data and Evidence\nOrganizations need robust data systems...",
    "## Workforce\nStaff capacity and skills development...",
    "## Partnerships\nCollaboration with stakeholders...",
    "## Gender Equality\nGender mainstreaming in programs...",
    "## Innovation\nAdopting new technologies and approaches..."
]

results = await gather(*[
    limit(sem, summarize_content(state, section_key, content)) 
    for theme in themes
])
print(results[0])

{
    'summary': 'The JI-HoA project experienced data gaps related to migration. The Regional Data Hub produced 
research and engaged with National Statistical Offices, harmonizing data collection methods. However, stakeholders 
indicated that further improvements in data gathering capacities are needed.',
    'key_findings': [
        'Significant gaps in migration data existed.',
        'The Regional Data Hub produced 20 research outputs.',
        'Data collection methodologies were harmonized across countries.',
        'Stakeholders believe additional steps are needed to improve data gathering capacities.'
    ]
}

Mapping a theme

source

Stage

 Stage (value, names=None, module=None, qualname=None, type=None, start=1)

Pipeline stage number.

Exported source

class Stage(Enum):
    "Pipeline stage number."
    STAGE1 = "stage1"
    STAGE2 = "stage2"
    STAGE3 = "stage3"
    def __str__(self): return self.value

We treat observability and LLM evaluation as core requirements for our mapping pipeline. While DSPy’s built-in dspy.inspect_history() provides valuable reasoning chains, we enhance it with structured metadata (report_id, phase, framework) to create comprehensive audit trails. This enriched tracing enables systematic evaluation of mapping accuracy, supports human evaluator annotation workflows, and provides the detailed context necessary for debugging and improving our LLM-based document analysis system.

We define below enum and configuration classes for pipeline tracing and validation. These provide structured metadata for audit trails and evaluation.

source

TraceContext

 TraceContext (report_id:str, stage:__main__.Stage,
               framework:evaluatr.frameworks.FrameworkInfo)

Context for tracing the mapping process

	Type	Details
report_id	str	Report identifier
stage	Stage	Pipeline stage number
framework	FrameworkInfo	Framework info (name, category, theme_id)

Exported source

class TraceContext(AttrDict):
    "Context for tracing the mapping process"
    def __init__(self, 
                 report_id:str,  # Report identifier
                 stage:Stage,  # Pipeline stage number
                 framework:FrameworkInfo,  # Framework info (name, category, theme_id)
                 ): 
        # self.run_id = str(uuid.uuid4())[:8]  # Short unique ID
        store_attr()

    def __repr__(self):
        return f"TraceContext(report_id={self.report_id}, stage={self.stage}, framework={self.framework})"

tr_ctx = TraceContext(
    report_id='49d2fba781b6a7c0d94577479636ee6f', 
    stage=Stage.STAGE1, 
    framework=FrameworkInfo(Framework.SRF, FrameworkCat.ENABLERS, "4")
    )

tr_ctx

TraceContext(report_id=49d2fba781b6a7c0d94577479636ee6f, stage=stage1, framework={'category': 'Enablers', 'theme_id': '4', 'name': 'SRF'})

source

setup_logger

 setup_logger (name, handler, level=20, **kwargs)

Helper function to setup a logger with common configuration

Exported source

def setup_logger(name, handler, level=logging.INFO, **kwargs):
    "Helper function to setup a logger with common configuration"
    logger = logging.getLogger(name)
    logger.handlers.clear()
    logger.addHandler(handler)
    logger.setLevel(level)
    for k,v in kwargs.items(): setattr(logger, k, v)
    return logger

source

setup_trace_logging

 setup_trace_logging (report_id, verbosity=1)

Exported source

def setup_trace_logging(report_id, verbosity=cfg.verbosity):
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    filename = f'{report_id}_{timestamp}.jsonl'
    file_handler = logging.FileHandler(traces_dir / filename, mode='w')
    setup_logger('trace.file', file_handler)    
    console_handler = logging.StreamHandler()
    setup_logger('trace.console', console_handler, verbosity=verbosity)

source

log_analysis_event

 log_analysis_event (event:str, trace_ctx:__main__.TraceContext,
                     **extra_data)

Log an analysis event to file and console with different verbosity levels

Exported source

def log_analysis_event(event: str, trace_ctx: TraceContext, **extra_data):
    """Log an analysis event to file and console with different verbosity levels"""
    file_logger = logging.getLogger('trace.file')
    console_logger = logging.getLogger('trace.console')
    
    base_data = {
        "timestamp": datetime.now().isoformat(),
        "event": event,
        "report_id": trace_ctx.report_id,
        "stage": str(trace_ctx.stage),
        "framework": str(trace_ctx.framework.name),
        "framework_category": str(trace_ctx.framework.category),
        "framework_theme_id": str(trace_ctx.framework.theme_id),
    }
    base_data.update(extra_data)
    
    # File logger - always full JSON
    file_logger.info(json.dumps(base_data, indent=2))
    
    # Console logger - verbosity-based formatting
    if hasattr(console_logger, 'verbosity'):
        if console_logger.verbosity == 1:
            console_msg = f"{base_data['report_id']} - {base_data['stage']}"
        elif console_logger.verbosity == 2:
            console_msg = f"{base_data['report_id']} - {base_data['stage']} - {base_data['event']}"
        else:  # verbosity == 3
            console_msg = json.dumps(base_data, indent=2)
        
        console_logger.info(console_msg)

async def analyze_theme(
    theme: str,
    sections_lookup: dict,
    hdgs: dict,
    semaphore: Semaphore,
    select_fn: Callable = select_section,
    summarize_fn: Callable = summarize_content,
    evaluate_fn: Callable = evaluate_evidence,
    log_fn: Callable = None,
    prior_coverage_context: str = "",
    max_iterations: int = 5,
    model: str = 'gemini/gemini-2.0-flash'
):
    "Analyze if a theme is covered in the evaluation report"
    log = log_fn or (lambda event, **kw: None)
    
    log("Starting Analysis", theme=theme)
    
    # Initialize state
    state = State(
        theme=theme,
        prior_coverage_context=prior_coverage_context,
        available_sections=list(sections_lookup.keys())
    )
    
    # Iterative exploration
    for i in range(max_iterations):
        log("Iteration Start", iteration=i+1)
        
        # Select section
        selected = await limit(semaphore, select_fn(state, model))
        log("Section Selected", 
            iteration=i+1,
            section=selected['section_key'], 
            reasoning=selected['reasoning'])
                
        # Extract content
        path = sections_lookup.get(selected['section_key'])
        if not path:
            log("Section Not Found", 
                iteration=i+1,
                section=selected['section_key'])
            continue
        content = get_content_tool(hdgs, path)
        
        # Summarize
        summary = await limit(semaphore, summarize_fn(state, selected['section_key'], content, model))
        log("Content Summarized", 
            iteration=i+1,
            section=selected['section_key'],
            summary=summary['summary'],
            key_findings=summary['key_findings'])
        
        # Update state with summary and section BEFORE evaluation
        state.section_summaries.append(summary)
        state.explored_sections.append(selected['section_key'])
        state.available_sections.remove(selected['section_key'])
        
        # Evaluate
        evaluation = await limit(semaphore, evaluate_fn(state, content, model))
        log("Evidence Evaluated",
            iteration=i+1,
            theme_covered=evaluation['theme_covered'],
            coverage_reasoning=evaluation['coverage_reasoning'],
            gaps_identified=evaluation['gaps_identified'],
            should_continue=evaluation['should_continue'])
        
        if len(state.evaluation_history) > 1:
            prev = state.evaluation_history[-2]
            log("Understanding Progression",
                iteration=i+1,
                previous_covered=prev['theme_covered'],
                current_covered=evaluation['theme_covered'],
                previous_gaps=prev['gaps_identified'],
                current_gaps=evaluation['gaps_identified'])
            
        # Update state with evaluation results
        state.evaluation_history.append({
            'iteration': i + 1,
            'theme_covered': evaluation['theme_covered'],
            'coverage_reasoning': evaluation['coverage_reasoning'],
            'gaps_identified': evaluation['gaps_identified']
        })
        
        state.theme_covered = evaluation['theme_covered']
        state.coverage_reasoning = evaluation['coverage_reasoning']
        state.gaps_identified = evaluation['gaps_identified']
            
        log("State Updated",
            iteration=i+1,
            explored_sections=state.explored_sections,
            evidence_count=len(state.section_summaries),
            remaining_sections=len(state.available_sections),
            theme_covered=state.theme_covered,
            current_gaps=state.gaps_identified)
        
        # Check stopping
        if not evaluation['should_continue']:
            state.stop_reason = "sufficient_evidence"
            log("Analysis Complete", 
                iteration=i+1,
                reason=state.stop_reason)
            break
    else:
        state.stop_reason = "max_iterations"
        log("Analysis Complete", 
            reason=state.stop_reason,
            iterations_completed=state.iterations_completed,
            theme_covered=state.theme_covered,
            final_reasoning=state.coverage_reasoning,
            sections_explored_count=len(state.explored_sections),
            total_sections=len(sections_lookup))

    
    return state

Single Theme Analysis

# 1. Setup trace logging
setup_trace_logging(report_id="49d2fba781b6a7c0d94577479636ee6f", verbosity=3)

# 2. Prepare document structure
hdgs = create_heading_dict(report)
sections_lookup = {key: path for key, path in flatten_sections(hdgs)}

# 3. Setup trace context
trace_ctx = TraceContext(
    report_id="49d2fba781b6a7c0d94577479636ee6f",
    stage=Stage.STAGE1,
    framework=FrameworkInfo(Framework.SRF, FrameworkCat.ENABLERS, "4")
)

# 4. Create log function
log_fn = lambda event, **kw: log_analysis_event(event, trace_ctx, **kw)

# 5. Prepare theme
theme = format_enabler_theme(eval_data.srf_enablers[3])  # Data and evidence
print(theme)

# 6. Run analysis
sem = Semaphore(30)
result = await analyze_theme(
    theme=theme,
    sections_lookup=sections_lookup,
    hdgs=hdgs,
    semaphore=sem,
    log_fn=log_fn
)

## Enabler 4: Data and evidence
### Description
IOM will be the pre-eminent source of migration and displacement data for action, which help save lives and deliver
solutions; data for insight, which help facilitate regular migration pathways; and data for foresight, which help 
drive anticipatory action. IOM will have the systems and data fluency to collect, safely store, analyze, share and 
apply disaggregated data and evidence across the mobility spectrum. Our extensive data and research repositories 
will underpin evidence-based policies and practices. Data will be central to the internal decision-making and 
management of the Organization.

{
  "timestamp": "2025-10-04T20:52:46.350161",
  "event": "Starting Analysis",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "theme": "## Enabler 4: Data and evidence\n### Description\nIOM will be the pre-eminent source of migration and displacement data for action, which help save lives and deliver solutions; data for insight, which help facilitate regular migration pathways; and data for foresight, which help drive anticipatory action. IOM will have the systems and data fluency to collect, safely store, analyze, share and apply disaggregated data and evidence across the mobility spectrum. Our extensive data and research repositories will underpin evidence-based policies and practices. Data will be central to the internal decision-making and management of the Organization."
}
{
  "timestamp": "2025-10-04T20:52:46.350906",
  "event": "Iteration Start",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 1
}
{
  "timestamp": "2025-10-04T20:52:47.206499",
  "event": "Section Selected",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 1,
  "section": "Data availability .... page 16",
  "reasoning": "The theme focuses on data and evidence. Section 24, titled 'Data availability', is the most likely to contain relevant information for this theme. It directly addresses the availability of data within the program being evaluated."
}
{
  "timestamp": "2025-10-04T20:52:49.521531",
  "event": "Content Summarized",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 1,
  "section": "Data availability .... page 16",
  "summary": "The evaluation found that the JI significantly improved data availability on human migration in the Horn of Africa, exceeding targets for field studies and research. The Regional Data Hub played a key role through data production, publication, and engagement with National Statistical Offices and regional stakeholders. Stakeholder capacity was also strengthened, with increased knowledge on return and reintegration issues reported. Data is being used in policymaking, but challenges remain. Stakeholders noted the need for further capacity strengthening and resources to effectively utilize the data. Staff turnover, financial constraints, and the COVID-19 pandemic hindered the optimal use of data and capacity for policymaking.",
  "key_findings": [
    "The JI exceeded targets for data production and research on migration in the Horn of Africa.",
    "Stakeholders reported increased knowledge on return and reintegration issues due to capacity building efforts.",
    "Data produced by the JI has supported evidence-based policies, procedures, and program design, exceeding initial targets.",
    "Increased number of stakeholders involved in return and reintegration assistance.",
    "Challenges such as staff turnover, financial constraints, and the COVID-19 pandemic hindered the optimal use of data and capacity for policymaking."
  ]
}
{
  "timestamp": "2025-10-04T20:52:50.933915",
  "event": "Evidence Evaluated",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 1,
  "theme_covered": true,
  "coverage_reasoning": "This section provides strong evidence for the 'Data and evidence' theme. It includes quantitative data (e.g., number of field studies, stakeholders trained, stakeholders reporting increased knowledge, number of actors involved in return and reintegration assistance), qualitative data (quotes from stakeholders in Djibouti and Sudan), and discusses outcomes (increased use of data in policymaking). It also includes critical analysis of challenges (staff turnover, financial constraints, COVID-19) and presents evidence from multiple sources (IOM logframe, stakeholder surveys, interviews, interim reports). The evidence shows results and impact, not just activities.",
  "gaps_identified": "None",
  "should_continue": true
}
{
  "timestamp": "2025-10-04T20:52:50.937108",
  "event": "State Updated",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 1,
  "explored_sections": [
    "Data availability .... page 16"
  ],
  "evidence_count": 1,
  "remaining_sections": 46,
  "theme_covered": true,
  "current_gaps": "None"
}
{
  "timestamp": "2025-10-04T20:52:50.938241",
  "event": "Iteration Start",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 2
}
{
  "timestamp": "2025-10-04T20:52:52.263495",
  "event": "Section Selected",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 2,
  "section": "3. Methodology .... page 8",
  "reasoning": "Since the theme is 'Data and evidence' and the previous iteration found sufficient coverage, it's still important to understand how the evaluation itself gathered and used data. Section 7, 'Methodology', is likely to contain information about the data collection and analysis methods used in the evaluation, which is directly relevant to the theme."
}
{
  "timestamp": "2025-10-04T20:52:53.955366",
  "event": "Content Summarized",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 2,
  "section": "3. Methodology .... page 8",
  "summary": "The evaluation of the JI-HoA initiative employed a mixed-methods approach, incorporating desk research, stakeholder interviews, and focus group discussions to gather both quantitative and qualitative data. The methodology was guided by OECD/DAC evaluation criteria, focusing on relevance, coherence, effectiveness, efficiency, and sustainability. Data collection involved primary and secondary sources, with an emphasis on triangulating data to identify trends, strengths, and weaknesses across the four countries. Logistical challenges, including security concerns and stakeholder availability, impacted the scope and implementation of data collection in certain regions.",
  "key_findings": [
    "The evaluation used a mixed-methods approach, combining desk research, stakeholder interviews, and focus group discussions.",
    "OECD/DAC evaluation criteria guided the evaluation process.",
    "Data triangulation was used to validate findings from secondary and primary data sources.",
    "Security concerns and stakeholder availability posed limitations to data collection in specific regions."
  ]
}
{
  "timestamp": "2025-10-04T20:52:55.745053",
  "event": "Evidence Evaluated",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 2,
  "theme_covered": true,
  "coverage_reasoning": "The 'Data and evidence' theme is well-supported by the evidence provided in this report. The methodology section details how data was collected and analyzed, including both quantitative and qualitative methods. It mentions desk research, stakeholder interviews, and focus group discussions. The section also discusses the use of OECD/DAC evaluation criteria, which implies a critical analysis of the data. The previous sections also provide quantitative and qualitative data related to the theme. The current section provides information about the limitations of the data collection process due to security concerns and stakeholder availability, which adds to the critical analysis. The evidence comes from multiple sources, including IOM staff, stakeholders, and desk research. The outcomes are reflected in the use of data for evaluation and recommendations for future programming, not just activities.",
  "gaps_identified": "None",
  "should_continue": true
}
{
  "timestamp": "2025-10-04T20:52:55.747260",
  "event": "State Updated",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 2,
  "explored_sections": [
    "Data availability .... page 16",
    "3. Methodology .... page 8"
  ],
  "evidence_count": 2,
  "remaining_sections": 45,
  "theme_covered": true,
  "current_gaps": "None"
}
{
  "timestamp": "2025-10-04T20:52:55.748012",
  "event": "Iteration Start",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 3
}
{
  "timestamp": "2025-10-04T20:52:57.076211",
  "event": "Section Selected",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 3,
  "section": "4.3.1. Specific Objective 1: Partner countries and relevant stakeholders developed or strengthened evidence-based return and reintegration procedures .... page 16",
  "reasoning": "The theme focuses on data and evidence. Section 4.3.1, which discusses Specific Objective 1 related to evidence-based procedures, is likely to contain relevant information. Since the theme has been covered in previous iterations, exploring sections directly related to objectives and outcomes is a good strategy."
}
{
  "timestamp": "2025-10-04T20:52:58.100536",
  "event": "Content Summarized",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 3,
  "section": "4.3.1. Specific Objective 1: Partner countries and relevant stakeholders developed or strengthened evidence-based return and reintegration procedures .... page 16",
  "summary": "Specific Objective 1 focuses on developing data and data collection methods, alongside training and dialogue, to enhance data availability and stakeholder capacity. This, in turn, aims to foster the creation of evidence-based return and reintegration procedures.",
  "key_findings": [
    "Development of data and data collection methodologies is key.",
    "Training and dialogue contribute to increased data availability.",
    "Strengthened stakeholder capacity results from these efforts.",
    "The ultimate goal is the development of evidence-based return and reintegration procedures."
  ]
}
{
  "timestamp": "2025-10-04T20:52:59.944163",
  "event": "Evidence Evaluated",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 3,
  "theme_covered": true,
  "coverage_reasoning": "This section reinforces the 'Data and evidence' theme. It highlights the development of data and data collection methodologies, training, and dialogue as key components for increasing data availability and strengthening stakeholder capacity. This ultimately leads to the development of evidence-based return and reintegration procedures. This aligns with the theme's focus on data-driven decision-making and evidence-based policies. The previous sections also provide quantitative and qualitative data related to the theme. The methodology section details how data was collected and analyzed, including both quantitative and qualitative methods. It mentions desk research, stakeholder interviews, and focus group discussions. The section also discusses the use of OECD/DAC evaluation criteria, which implies a critical analysis of the data. The previous sections also provide quantitative and qualitative data related to the theme. The current section provides information about the limitations of the data collection process due to security concerns and stakeholder availability, which adds to the critical analysis. The evidence comes from multiple sources, including IOM staff, stakeholders, and desk research. The outcomes are reflected in the use of data for evaluation and recommendations for future programming, not just activities. The evidence shows results and impact, not just activities. The evidence includes discussion of challenges, limitations, or areas needing improvement. The evidence comes from at least 2 different report sections.",
  "gaps_identified": "None",
  "should_continue": true
}
{
  "timestamp": "2025-10-04T20:52:59.947515",
  "event": "Understanding Progression",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 3,
  "previous_covered": true,
  "current_covered": true,
  "previous_gaps": "None",
  "current_gaps": "None"
}
{
  "timestamp": "2025-10-04T20:52:59.948435",
  "event": "State Updated",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 3,
  "explored_sections": [
    "Data availability .... page 16",
    "3. Methodology .... page 8",
    "4.3.1. Specific Objective 1: Partner countries and relevant stakeholders developed or strengthened evidence-based return and reintegration procedures .... page 16"
  ],
  "evidence_count": 3,
  "remaining_sections": 44,
  "theme_covered": true,
  "current_gaps": "None"
}
{
  "timestamp": "2025-10-04T20:52:59.949193",
  "event": "Iteration Start",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 4
}
{
  "timestamp": "2025-10-04T20:53:00.865953",
  "event": "Section Selected",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 4,
  "section": "5.1. Conclusions .... page 27",
  "reasoning": "Since the theme is already covered and no gaps were identified in previous iterations, I will check the 'Conclusions' section to see if there is any high-level information related to data and evidence that might be relevant."
}
{
  "timestamp": "2025-10-04T20:53:02.541554",
  "event": "Content Summarized",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 4,
  "section": "5.1. Conclusions .... page 27",
  "summary": "The EU-IOM Joint Initiative for Migrant Protection and Reintegration in the Horn of Africa significantly contributed to data and research on migration trends. The Regional Data Hub's data production and capacity building were valued by stakeholders, with the potential for long-term positive impacts on policy and decision-making. The Initiative's research provided crucial evidence for programming, informing IOM, partners, and stakeholders about returnees' needs and effective approaches. However, the program's ownership and sustainability are challenged by the complex context and fragility of systems in the Horn of Africa, requiring continued support for capacity-building and broader socio-economic development.",
  "key_findings": [
    "The Regional Data Hub's data production and capacity building were explicitly appreciated by stakeholders.",
    "The dissemination and use of data can positively impact return and reintegration by providing governments and stakeholders with tools and capacity for policy and decision-making.",
    "JI-HoA research provided important evidence for programming, informing IOM, partners, and stakeholders about returnees\u2019 needs and effective approaches."
  ]
}
{
  "timestamp": "2025-10-04T20:53:04.850067",
  "event": "Evidence Evaluated",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 4,
  "theme_covered": true,
  "coverage_reasoning": "This section provides further evidence for the 'Data and evidence' theme. It highlights the contributions to data availability and research on migration trends, specifically mentioning the Regional Data Hub's role in data production and capacity building, which stakeholders appreciated. The section also notes that the JI-HoA's research provided important evidence for programming, informing IOM, partners, and stakeholders about returnees' needs and effective approaches. This aligns with the theme's focus on data-driven decision-making and evidence-based policies. The section includes critical analysis, discussing the challenges to ownership and sustainability due to the complex context and fragility of systems in the Horn of Africa. It also mentions how capacity-building efforts were undermined by political priorities and staff turnover. The evidence comes from multiple sources, including desk research, interviews, and focus group discussions. The outcomes are reflected in the use of data for policy- and decision-making and informing migrant protection, return and reintegration programming. Quantitative evidence is present in the discussion of the Regional Data Hub's data production. Qualitative evidence is present in the stakeholder appreciation of the Regional Data Hub and the use of research to inform programming. The discussion includes challenges, limitations, or areas needing improvement, such as the need for continued support for capacity-building and broader socio-economic development. Evidence comes from multiple sources, including desk research, interviews, and focus group discussions, as well as the Regional Data Hub.",
  "gaps_identified": "None",
  "should_continue": false
}
{
  "timestamp": "2025-10-04T20:53:04.853804",
  "event": "Understanding Progression",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 4,
  "previous_covered": true,
  "current_covered": true,
  "previous_gaps": "None",
  "current_gaps": "None"
}
{
  "timestamp": "2025-10-04T20:53:04.854939",
  "event": "State Updated",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 4,
  "explored_sections": [
    "Data availability .... page 16",
    "3. Methodology .... page 8",
    "4.3.1. Specific Objective 1: Partner countries and relevant stakeholders developed or strengthened evidence-based return and reintegration procedures .... page 16",
    "5.1. Conclusions .... page 27"
  ],
  "evidence_count": 4,
  "remaining_sections": 43,
  "theme_covered": true,
  "current_gaps": "None"
}
{
  "timestamp": "2025-10-04T20:53:04.856808",
  "event": "Analysis Complete",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 4,
  "reason": "sufficient_evidence"
}

print(f"Coverage reasoning: {result.coverage_reasoning}")
print(f"Gaps identified: {result.gaps_identified}")

Coverage reasoning: This section provides further evidence for the 'Data and evidence' theme. It highlights the
contributions to data availability and research on migration trends, specifically mentioning the Regional Data
Hub's role in data production and capacity building, which stakeholders appreciated. The section also notes that
the JI-HoA's research provided important evidence for programming, informing IOM, partners, and stakeholders about
returnees' needs and effective approaches. This aligns with the theme's focus on data-driven decision-making and
evidence-based policies. The section includes critical analysis, discussing the challenges to ownership and
sustainability due to the complex context and fragility of systems in the Horn of Africa. It also mentions how
capacity-building efforts were undermined by political priorities and staff turnover. The evidence comes from
multiple sources, including desk research, interviews, and focus group discussions. The outcomes are reflected in
the use of data for policy- and decision-making and informing migrant protection, return and reintegration
programming. Quantitative evidence is present in the discussion of the Regional Data Hub's data production.
Qualitative evidence is present in the stakeholder appreciation of the Regional Data Hub and the use of research to
inform programming. The discussion includes challenges, limitations, or areas needing improvement, such as the need
for continued support for capacity-building and broader socio-economic development. Evidence comes from multiple
sources, including desk research, interviews, and focus group discussions, as well as the Regional Data Hub.

Gaps identified: None

Multiple Theme Analysis in parallel

# Setup shared logging (one file for the report)
setup_trace_logging(report_id="49d2fba781b6a7c0d94577479636ee6f", verbosity=3)

# Prepare document structure (shared)
hdgs = create_heading_dict(report)
sections_lookup = {key: path for key, path in flatten_sections(hdgs)}

# Create trace contexts for two different themes
trace_ctx1 = TraceContext(
    report_id="49d2fba781b6a7c0d94577479636ee6f",
    stage=Stage.STAGE1,
    framework=FrameworkInfo(Framework.SRF, FrameworkCat.ENABLERS, "4")  # Data and evidence
)
trace_ctx2 = TraceContext(
    report_id="49d2fba781b6a7c0d94577479636ee6f",
    stage=Stage.STAGE1,
    framework=FrameworkInfo(Framework.SRF, FrameworkCat.ENABLERS, "1")  # Workforce
)

# Prepare themes
theme1 = format_enabler_theme(eval_data.srf_enablers[3])  # Data and evidence
theme2 = format_enabler_theme(eval_data.srf_enablers[0])  # Workforce

# Run in parallel with shared semaphore
sem = Semaphore(cfg.semaphore)
log_fn1 = lambda event, **kw: log_analysis_event(event, trace_ctx1, **kw)
log_fn2 = lambda event, **kw: log_analysis_event(event, trace_ctx2, **kw)

results = await gather(
    analyze_theme(theme1, sections_lookup, hdgs, sem, log_fn=log_fn1),
    analyze_theme(theme2, sections_lookup, hdgs, sem, log_fn=log_fn2)
)

{
  "timestamp": "2025-10-04T19:32:39.755851",
  "event": "Starting Analysis",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "theme": "## Enabler 4: Data and evidence\n### Description\nIOM will be the pre-eminent source of migration and displacement data for action, which help save lives and deliver solutions; data for insight, which help facilitate regular migration pathways; and data for foresight, which help drive anticipatory action. IOM will have the systems and data fluency to collect, safely store, analyze, share and apply disaggregated data and evidence across the mobility spectrum. Our extensive data and research repositories will underpin evidence-based policies and practices. Data will be central to the internal decision-making and management of the Organization."
}
{
  "timestamp": "2025-10-04T19:32:39.756443",
  "event": "Iteration Start",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 1
}
{
  "timestamp": "2025-10-04T19:32:39.758227",
  "event": "Starting Analysis",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "theme": "## Enabler 1: Workforce\n### Description\nIOM\u2019s diverse and capable people are our most valued asset. Through investing in better workforce planning and people management, we will facilitate their professional development and improve their daily workplace experience. IOM will have flexible systems and procedures in place to ensure it can adapt to the future of work while ensuring the safety, security and well-being of its staff. IOM will strengthen its security posture and foster an inclusive and enabling work environment. Our leadership will have the ability to connect, motivate and inspire a sense of our shared vision and values."
}
{
  "timestamp": "2025-10-04T19:32:39.761754",
  "event": "Iteration Start",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 1
}
{
  "timestamp": "2025-10-04T19:32:40.615869",
  "event": "Section Selected",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 1,
  "section": "Data availability .... page 16",
  "reasoning": "The theme is 'Data and evidence'. Section 24 is titled 'Data availability', which is directly relevant to the theme. It's the most promising section to start with since no sections have been explored yet."
}
{
  "timestamp": "2025-10-04T19:32:40.816124",
  "event": "Section Selected",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 1,
  "section": "1. Introduction .... page 4",
  "reasoning": "As this is the initial exploration, starting with the 'Introduction' section will provide a general overview of the document and its purpose. This will help to understand the context and scope of the evaluation, which is crucial before diving into specific findings related to the 'Workforce' theme."
}
{
  "timestamp": "2025-10-04T19:32:42.699068",
  "event": "Content Summarized",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 1,
  "section": "Data availability .... page 16",
  "summary": "The evaluation found that the JI significantly improved data availability on human migration in the Horn of Africa, exceeding targets for field studies and research. The Regional Data Hub played a key role through data production, publication, and engagement with National Statistical Offices and regional stakeholders. Stakeholder capacity was also strengthened, with increased knowledge on return and reintegration issues reported. Data is being used in policymaking, but challenges remain. Stakeholders noted the need for further capacity strengthening and resources to effectively utilize the data. Staff turnover, financial constraints, and the COVID-19 pandemic hindered the optimal use of data and capacity for policymaking.",
  "key_findings": [
    "The JI exceeded targets for data production and research on migration in the Horn of Africa.",
    "Stakeholders reported increased knowledge on return and reintegration issues due to capacity building efforts.",
    "Data produced by the JI has supported evidence-based policies, procedures, and program design, exceeding initial targets.",
    "Increased number of stakeholders involved in return and reintegration assistance.",
    "Challenges such as staff turnover, financial constraints, and the COVID-19 pandemic hindered the optimal use of data and capacity for policymaking."
  ]
}
{
  "timestamp": "2025-10-04T19:32:43.171033",
  "event": "Content Summarized",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 1,
  "section": "1. Introduction .... page 4",
  "summary": "The EU-IOM Joint Initiative for Migrant Protection and Reintegration (JI) launched in 2016 with the objective of facilitating orderly, safe, regular, and rights-based migration. The JI-HoA commenced in March 2017 in the Khartoum Process countries, focusing on Djibouti, Ethiopia, Somalia, and Sudan, and was coordinated by a Regional Coordination Unit (RCU) based in Nairobi, Kenya. A final independent evaluation was conducted in 2022/2023, covering the 2017-2022 period, assessing the program's relevance, effectiveness, efficiency, coherence, and sustainability. The evaluation used desk research, stakeholder interviews, beneficiary focus groups, and a validation workshop, and also drew upon existing M&E data. The report presents findings against evaluation criteria and provides conclusions and recommendations for future programming.",
  "key_findings": [
    "The EU-IOM Joint Initiative for Migrant Protection and Reintegration (JI) aimed to facilitate orderly, safe, regular, and rights-based migration.",
    "The JI-HoA focused on Djibouti, Ethiopia, Somalia, and Sudan, with coordination by a Regional Coordination Unit (RCU) in Nairobi, Kenya.",
    "The final evaluation assessed the program's relevance, effectiveness, efficiency, coherence, and sustainability.",
    "Data collection methods included desk research, stakeholder interviews, beneficiary focus groups, a validation workshop, and existing M&E data."
  ]
}
{
  "timestamp": "2025-10-04T19:32:44.853024",
  "event": "Evidence Evaluated",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 1,
  "theme_covered": true,
  "coverage_reasoning": "The report provides strong evidence that the Joint Initiative (JI) significantly improved data availability and capacity building related to migration in the Horn of Africa. Quantitative data from the IOM logframe demonstrates that targets for field studies, stakeholder engagement, and capacity building were exceeded. Stakeholder surveys confirm increased knowledge on return and reintegration issues. The report also provides concrete examples of data being used in policymaking, such as the Women and Social Affairs Ministry in Ethiopia using the national returnee database. Figure 4 visually represents the increase in actors involved in return and reintegration assistance. While challenges like staff turnover, financial constraints, and COVID-19 hindered optimal use, the core concept of improved data and evidence is well-supported by verifiable data and stakeholder feedback.",
  "gaps_identified": "While the report demonstrates increased data availability and capacity, it also acknowledges challenges in translating these into effective policymaking due to staff turnover, financial constraints, and external factors like COVID-19. Further exploration could focus on the sustainability of these data initiatives and the long-term impact on policy outcomes.",
  "should_continue": false
}
{
  "timestamp": "2025-10-04T19:32:44.855219",
  "event": "State Updated",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 1,
  "explored_sections": [
    "Data availability .... page 16"
  ],
  "evidence_count": 1,
  "remaining_sections": 46,
  "theme_covered": true,
  "current_gaps": "While the report demonstrates increased data availability and capacity, it also acknowledges challenges in translating these into effective policymaking due to staff turnover, financial constraints, and external factors like COVID-19. Further exploration could focus on the sustainability of these data initiatives and the long-term impact on policy outcomes."
}
{
  "timestamp": "2025-10-04T19:32:44.856571",
  "event": "Analysis Complete",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "4",
  "iteration": 1,
  "reason": "sufficient_evidence"
}
{
  "timestamp": "2025-10-04T19:32:44.980698",
  "event": "Evidence Evaluated",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 1,
  "theme_covered": false,
  "coverage_reasoning": "The provided section (Introduction) does not address the 'Workforce' theme. It focuses on the EU-IOM Joint Initiative for Migrant Protection and Reintegration, its objectives, evaluation methodology, and geographical scope. There is no mention of IOM's workforce, workforce planning, staff development, workplace experience, security, or leadership. The section primarily describes the program being evaluated and the evaluation's approach.",
  "gaps_identified": "The report has not yet addressed the 'Workforce' theme. There is no information on workforce planning, staff development, workplace experience, security measures for staff, or leadership capabilities within IOM in relation to this program.",
  "should_continue": true
}
{
  "timestamp": "2025-10-04T19:32:44.982430",
  "event": "State Updated",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 1,
  "explored_sections": [
    "1. Introduction .... page 4"
  ],
  "evidence_count": 1,
  "remaining_sections": 46,
  "theme_covered": false,
  "current_gaps": "The report has not yet addressed the 'Workforce' theme. There is no information on workforce planning, staff development, workplace experience, security measures for staff, or leadership capabilities within IOM in relation to this program."
}
{
  "timestamp": "2025-10-04T19:32:44.983155",
  "event": "Iteration Start",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 2
}
{
  "timestamp": "2025-10-04T19:32:46.078204",
  "event": "Section Selected",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 2,
  "section": "4. Findings .... page 10",
  "reasoning": "The 'Findings' section is the most likely place to find information related to the 'Workforce' theme, as it should contain the core results and observations of the evaluation. Since the theme hasn't been addressed yet, starting with the main findings section is a logical step."
}
{
  "timestamp": "2025-10-04T19:32:48.665117",
  "event": "Content Summarized",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 2,
  "section": "4. Findings .... page 10",
  "summary": "The JI-HoA program demonstrated relevance to the needs of migrants, returnees, communities, and stakeholders by providing essential support and addressing critical gaps in migration management. It aligned with IOM's objectives, EU foreign policy, and regional frameworks, fostering collaboration and avoiding duplication. However, challenges remain in ensuring the consistency and adequacy of support, particularly in psychosocial services and community involvement.",
  "key_findings": [
    "The program effectively addressed the immediate needs of migrants, such as food, water, and shelter, but faced challenges in timely service provision and AVRR, leading to dissatisfaction among some migrants.",
    "The integrated approach to economic, social, and psychosocial support was highly relevant to the reintegration needs of returnees, addressing economic vulnerability, stigma, and mental health issues.",
    "Economic assistance was crucial for returnees' reintegration, restoring dignity and enabling income generation, but the overall value was sometimes insufficient, and the support did not always align with recipients' knowledge or local context.",
    "The program addressed community needs by creating economic opportunities and reducing the drive for migration, with the majority of community members believing that the projects addressed their needs and those of returnees.",
    "The program significantly contributed to building migration management frameworks and capacity in participating countries, particularly in data collection and harmonization.",
    "The JI-HoA aligned with IOM's objectives, EU foreign policy, and regional frameworks, fostering collaboration and avoiding duplication with other initiatives.",
    "Gaps were identified in post-return psychosocial support, including a lack of MHPSS services, unclear information about compensation, high costs, stigma, and low awareness among communities."
  ]
}
{
  "timestamp": "2025-10-04T19:32:50.686941",
  "event": "Evidence Evaluated",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 2,
  "theme_covered": false,
  "coverage_reasoning": "The new content focuses on the relevance and coherence of the JI-HoA program, assessing its alignment with the needs of migrants, returnees, communities, governments, and other stakeholders, as well as its alignment with IOM and EU objectives. While the program's effectiveness in addressing the needs of migrants and returnees is discussed, there is no information about the IOM workforce, workforce planning, staff development, workplace experience, security measures for staff, or leadership capabilities within IOM. The section primarily describes the program being evaluated and its relevance to various stakeholders, but it does not provide any insights into the 'Workforce' theme as defined.",
  "gaps_identified": "The report has not yet addressed the 'Workforce' theme. There is no information on workforce planning, staff development, workplace experience, security measures for staff, or leadership capabilities within IOM in relation to this program.",
  "should_continue": true
}
{
  "timestamp": "2025-10-04T19:32:50.689344",
  "event": "State Updated",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 2,
  "explored_sections": [
    "1. Introduction .... page 4",
    "4. Findings .... page 10"
  ],
  "evidence_count": 2,
  "remaining_sections": 45,
  "theme_covered": false,
  "current_gaps": "The report has not yet addressed the 'Workforce' theme. There is no information on workforce planning, staff development, workplace experience, security measures for staff, or leadership capabilities within IOM in relation to this program."
}
{
  "timestamp": "2025-10-04T19:32:50.690125",
  "event": "Iteration Start",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 3
}
{
  "timestamp": "2025-10-04T19:32:51.811720",
  "event": "Section Selected",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 3,
  "section": "2. Background of the JI-HoA .... page 5",
  "reasoning": "Section 3, \"2. Background of the JI-HoA .... page 5\", might provide context on the program's design and implementation, potentially touching upon workforce considerations, even if indirectly. It's a high-level section that could offer a foundation for understanding how the workforce theme might be relevant."
}
{
  "timestamp": "2025-10-04T19:32:53.977400",
  "event": "Content Summarized",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 3,
  "section": "2. Background of the JI-HoA .... page 5",
  "summary": "The JI-HoA program, launched in 2017, aimed to facilitate safe and regular migration in the Horn of Africa, focusing on Djibouti, Ethiopia, Somalia, and Sudan. It operated with a budget that increased from an initial 25 million EUR to 64.7 million EUR. The program uses IOM's integrated approach to reintegration, addressing individual, community, and structural levels through economic, social, and psychosocial dimensions. Implementation faced challenges including the COVID-19 pandemic, the Russia-Ukraine conflict's economic impacts, and country-specific issues like conflict in Ethiopia, security concerns in Somalia, and instability in Sudan.",
  "key_findings": [
    "African migration significantly increased between 2010 and 2019, particularly in the IGAD region.",
    "The EU-IOM Joint Initiative for Migrant Protection and Reintegration in the Horn of Africa (JI-HoA) was launched in March 2017.",
    "The JI-HoA's budget increased fivefold to 64.7 million EUR over its lifespan.",
    "IOM's integrated approach to reintegration targets individual, community, and structural levels with economic, social, and psychosocial dimensions.",
    "The COVID-19 pandemic, the Russia-Ukraine conflict, and country-specific conflicts and instability affected the implementation of the JI-HoA."
  ]
}
{
  "timestamp": "2025-10-04T19:32:55.770207",
  "event": "Evidence Evaluated",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 3,
  "theme_covered": false,
  "coverage_reasoning": "This section provides background information on the JI-HoA program, including migration trends, the program's objectives, budget, and IOM's integrated approach to reintegration. It also discusses external factors that affected the program's implementation, such as the COVID-19 pandemic, the Russia-Ukraine conflict, and country-specific events. However, it does not contain any information related to the 'Workforce' theme. There is no mention of IOM's workforce, workforce planning, staff development, workplace experience, security measures for staff, or leadership capabilities within IOM. The section focuses on the program's context, design, and external challenges, but it does not provide any insights into the 'Workforce' theme as defined.",
  "gaps_identified": "The report has not yet addressed the 'Workforce' theme. There is no information on workforce planning, staff development, workplace experience, security measures for staff, or leadership capabilities within IOM in relation to this program.",
  "should_continue": true
}
{
  "timestamp": "2025-10-04T19:32:55.773429",
  "event": "Understanding Progression",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 3,
  "previous_covered": false,
  "current_covered": false,
  "previous_gaps": "The report has not yet addressed the 'Workforce' theme. There is no information on workforce planning, staff development, workplace experience, security measures for staff, or leadership capabilities within IOM in relation to this program.",
  "current_gaps": "The report has not yet addressed the 'Workforce' theme. There is no information on workforce planning, staff development, workplace experience, security measures for staff, or leadership capabilities within IOM in relation to this program."
}
{
  "timestamp": "2025-10-04T19:32:55.774195",
  "event": "State Updated",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 3,
  "explored_sections": [
    "1. Introduction .... page 4",
    "4. Findings .... page 10",
    "2. Background of the JI-HoA .... page 5"
  ],
  "evidence_count": 3,
  "remaining_sections": 44,
  "theme_covered": false,
  "current_gaps": "The report has not yet addressed the 'Workforce' theme. There is no information on workforce planning, staff development, workplace experience, security measures for staff, or leadership capabilities within IOM in relation to this program."
}
{
  "timestamp": "2025-10-04T19:32:55.775761",
  "event": "Iteration Start",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 4
}
{
  "timestamp": "2025-10-04T19:32:56.828632",
  "event": "Section Selected",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 4,
  "section": "5.2. Recommendations .... page 28",
  "reasoning": "The 'Recommendations' section (5.2) might contain information about improving IOM's workforce or addressing challenges related to staff, which could be relevant to the 'Workforce' theme. It's worth exploring to see if any recommendations touch upon workforce planning, staff development, or workplace environment."
}
{
  "timestamp": "2025-10-04T19:32:59.322357",
  "event": "Content Summarized",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 4,
  "section": "5.2. Recommendations .... page 28",
  "summary": "Recommendations emphasize enhancing capacity building and ownership among national, regional, and local stakeholders, expanding capacity building to include all relevant organizational departments and levels, and integrating migrant protection, return, and reintegration into policy documents and development plans. Community-based reintegration (CBR) projects should be strengthened to address drivers of irregular migration and provide economic opportunities, with increased involvement of local authorities. Partnerships with service providers who can function without significant IOM funding should be prioritized to strengthen referral systems and service provision quality. Continued funding for key activities like direct assistance in transit, integrated reintegration approaches, and the Regional Data Hub is crucial. Expanding support for returnees to include longer-term integration through education and entrepreneurial skill development is recommended. Continued support for safe migration pathways and investment in AVR are necessary, along with ongoing impact evaluations to measure reintegration sustainability.",
  "key_findings": [
    "Capacity building and ownership among stakeholders needs enhancement for sustainability.",
    "CBR projects require strengthening in design, M&E, and relevance to community needs.",
    "Partnerships with service providers who can function independently are crucial for long-term impact.",
    "Continued funding is needed for key activities like AVR and the Regional Data Hub.",
    "Support for returnees should be extended to include longer-term integration aspects.",
    "Continued investment in AVR is essential due to ongoing human trafficking and abuse.",
    "Impact evaluations should continue to refine reintegration sustainability measurement."
  ]
}
{
  "timestamp": "2025-10-04T19:33:01.566026",
  "event": "Evidence Evaluated",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 4,
  "theme_covered": false,
  "coverage_reasoning": "While this section provides recommendations for improving migrant protection, return, and reintegration programming, it does not directly address the 'Workforce' theme as defined. The recommendations focus on enhancing capacity building and ownership among stakeholders, strengthening community-based reintegration efforts, building partnerships with service providers, and exploring opportunities for continued funding of key activities. Although the section mentions capacity building, it is in the context of external stakeholders and not IOM's internal workforce. There is no discussion of IOM's workforce planning, staff development, workplace experience, security measures for staff, or leadership capabilities within IOM. The section primarily focuses on improving the program's effectiveness and sustainability through external partnerships and capacity building, but it does not provide any insights into the 'Workforce' theme as defined.",
  "gaps_identified": "The report has not yet addressed the 'Workforce' theme. There is no information on workforce planning, staff development, workplace experience, security measures for staff, or leadership capabilities within IOM in relation to this program.",
  "should_continue": true
}
{
  "timestamp": "2025-10-04T19:33:01.568829",
  "event": "Understanding Progression",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 4,
  "previous_covered": false,
  "current_covered": false,
  "previous_gaps": "The report has not yet addressed the 'Workforce' theme. There is no information on workforce planning, staff development, workplace experience, security measures for staff, or leadership capabilities within IOM in relation to this program.",
  "current_gaps": "The report has not yet addressed the 'Workforce' theme. There is no information on workforce planning, staff development, workplace experience, security measures for staff, or leadership capabilities within IOM in relation to this program."
}
{
  "timestamp": "2025-10-04T19:33:01.569868",
  "event": "State Updated",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 4,
  "explored_sections": [
    "1. Introduction .... page 4",
    "4. Findings .... page 10",
    "2. Background of the JI-HoA .... page 5",
    "5.2. Recommendations .... page 28"
  ],
  "evidence_count": 4,
  "remaining_sections": 43,
  "theme_covered": false,
  "current_gaps": "The report has not yet addressed the 'Workforce' theme. There is no information on workforce planning, staff development, workplace experience, security measures for staff, or leadership capabilities within IOM in relation to this program."
}
{
  "timestamp": "2025-10-04T19:33:01.570794",
  "event": "Iteration Start",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 5
}
{
  "timestamp": "2025-10-04T19:33:02.413606",
  "event": "Section Selected",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 5,
  "section": "3. Methodology .... page 8",
  "reasoning": "The theme 'Workforce' hasn't been addressed yet. Section 5, '3. Methodology', might contain information about the staff involved in the program's evaluation and implementation, which could provide insights into workforce planning, staff development, or workplace experience, even if indirectly."
}
{
  "timestamp": "2025-10-04T19:33:03.814823",
  "event": "Content Summarized",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 5,
  "section": "3. Methodology .... page 8",
  "summary": "The evaluation of the JI-HoA used a mixed-methods approach, incorporating desk research, interviews, and focus group discussions (FGDs) to gather data from IOM staff, stakeholders, and beneficiaries across four countries. The evaluation was guided by OECD/DAC criteria (Relevance, Coherence, Effectiveness, Efficiency, and Sustainability). Challenges included security and political instability in certain regions, which limited access to some program locations and stakeholders.",
  "key_findings": [
    "The evaluation used a mixed-methods approach, including desk research, interviews, and focus group discussions.",
    "Data was collected from IOM staff, stakeholders, and beneficiaries across four countries.",
    "The evaluation was guided by OECD/DAC criteria.",
    "Security and political instability limited access to some program locations and stakeholders."
  ]
}
{
  "timestamp": "2025-10-04T19:33:06.115936",
  "event": "Evidence Evaluated",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 5,
  "theme_covered": false,
  "coverage_reasoning": "This section describes the methodology used for the evaluation, including desk research, interviews, and focus group discussions. It mentions interviews with IOM staff at national and regional levels (21 interviews with 30 staff). However, the content does not provide any details about workforce planning, staff development, workplace experience, security measures, or leadership capabilities. The interviews with IOM staff are mentioned as a data collection method, but the findings from these interviews are not presented in this section in a way that addresses the 'Workforce' theme. The focus is on the evaluation methodology itself, not on the characteristics or experiences of the IOM workforce.",
  "gaps_identified": "The report has not yet addressed the 'Workforce' theme. There is no information on workforce planning, staff development, workplace experience, security measures for staff, or leadership capabilities within IOM in relation to this program. While IOM staff were interviewed, the results of those interviews are not presented in a way that addresses the theme.",
  "should_continue": true
}
{
  "timestamp": "2025-10-04T19:33:06.118833",
  "event": "Understanding Progression",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 5,
  "previous_covered": false,
  "current_covered": false,
  "previous_gaps": "The report has not yet addressed the 'Workforce' theme. There is no information on workforce planning, staff development, workplace experience, security measures for staff, or leadership capabilities within IOM in relation to this program.",
  "current_gaps": "The report has not yet addressed the 'Workforce' theme. There is no information on workforce planning, staff development, workplace experience, security measures for staff, or leadership capabilities within IOM in relation to this program. While IOM staff were interviewed, the results of those interviews are not presented in a way that addresses the theme."
}
{
  "timestamp": "2025-10-04T19:33:06.120087",
  "event": "State Updated",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "iteration": 5,
  "explored_sections": [
    "1. Introduction .... page 4",
    "4. Findings .... page 10",
    "2. Background of the JI-HoA .... page 5",
    "5.2. Recommendations .... page 28",
    "3. Methodology .... page 8"
  ],
  "evidence_count": 5,
  "remaining_sections": 42,
  "theme_covered": false,
  "current_gaps": "The report has not yet addressed the 'Workforce' theme. There is no information on workforce planning, staff development, workplace experience, security measures for staff, or leadership capabilities within IOM in relation to this program. While IOM staff were interviewed, the results of those interviews are not presented in a way that addresses the theme."
}
{
  "timestamp": "2025-10-04T19:33:06.121384",
  "event": "Analysis Complete",
  "report_id": "49d2fba781b6a7c0d94577479636ee6f",
  "stage": "stage1",
  "framework": "SRF",
  "framework_category": "Enablers",
  "framework_theme_id": "1",
  "reason": "max_iterations",
  "iterations_completed": 0,
  "theme_covered": false,
  "final_reasoning": "This section describes the methodology used for the evaluation, including desk research, interviews, and focus group discussions. It mentions interviews with IOM staff at national and regional levels (21 interviews with 30 staff). However, the content does not provide any details about workforce planning, staff development, workplace experience, security measures, or leadership capabilities. The interviews with IOM staff are mentioned as a data collection method, but the findings from these interviews are not presented in this section in a way that addresses the 'Workforce' theme. The focus is on the evaluation methodology itself, not on the characteristics or experiences of the IOM workforce.",
  "sections_explored_count": 5,
  "total_sections": 47
}

print(f"Theme 1 covered: {results[0].theme_covered}")
print(f"Theme 2 covered: {results[1].theme_covered}")

Theme 1 covered: True

Theme 2 covered: False

Pipeline Orchestrator

source

AnalysisResult

 AnalysisResult (state:__main__.State,
                 framework_info:evaluatr.frameworks.FrameworkInfo)

Extracts key results from State with framework metadata

Exported source

class AnalysisResult(AttrDict):
    "Extracts key results from State with framework metadata"
    def __init__(self, state: State, framework_info: FrameworkInfo):
        self.theme_covered = state.theme_covered
        self.coverage_reasoning = state.coverage_reasoning
        self.gaps_identified = state.gaps_identified
        self.explored_sections = state.explored_sections
        self.framework_name = framework_info.name
        self.framework_category = framework_info.category
        self.framework_theme_id = framework_info.theme_id

source

PipelineResults

 PipelineResults ()

Exported source

class PipelineResults(dict):
    def __init__(self):
        super().__init__()
        self[Stage.STAGE1] = defaultdict(lambda: defaultdict(dict))
        self[Stage.STAGE2] = defaultdict(lambda: defaultdict(dict))
        self[Stage.STAGE3] = defaultdict(lambda: defaultdict(dict))

source

PipelineResults.call

 PipelineResults.__call__ (stage=<Stage.STAGE1: 'stage1'>,
                           filter_type='all')

Call self as a function.

Exported source

@patch
def __call__(self:PipelineResults, stage=Stage.STAGE1, filter_type="all"):
    themes = []
    for frameworks in self[stage].values():
        for categories in frameworks.values():
            for theme in categories.values():
                if filter_type == "all" or \
                   (filter_type == "covered" and theme.theme_covered) or \
                   (filter_type == "uncovered" and not theme.theme_covered):
                    themes.append(theme)
    return themes

source

PipelineOrchestrator

 PipelineOrchestrator (report_id:str, headings:dict,
                       get_content_fn:Callable,
                       eval_data:evaluatr.frameworks.EvalData,
                       verbosity:int=2)

Orchestrator for the IOM evaluation report mapping pipeline

	Type	Default	Details
report_id	str		Report identifier
headings	dict		Report headings
get_content_fn	Callable		Function to get the content of a section
eval_data	EvalData		Evaluation data
verbosity	int	2	Verbosity level

Exported source

class PipelineOrchestrator:
    "Orchestrator for the IOM evaluation report mapping pipeline"
    def __init__(self, 
                 report_id:str, # Report identifier
                 headings:dict, # Report headings
                 get_content_fn:Callable, # Function to get the content of a section
                 eval_data:EvalData, # Evaluation data
                 verbosity:int=2, # Verbosity level
                 ):
        store_attr()
        setup_trace_logging(report_id, verbosity)
        self.results = PipelineResults()
        self.sections_lookup = {key: path for key, path in flatten_sections(headings)}

source

PipelineOrchestrator.analyze_with_context

 PipelineOrchestrator.analyze_with_context (theme:str,
                                            trace_ctx:__main__.TraceContex
                                            t, semaphore:asyncio.locks.Sem
                                            aphore,
                                            prior_coverage_context:str='')

Analyze theme with orchestrator context and return wrapped result

Exported source

@patch
async def analyze_with_context(
    self: PipelineOrchestrator,
    theme: str,
    trace_ctx: TraceContext,
    semaphore: Semaphore,
    prior_coverage_context: str = ""
) -> AnalysisResult:
    "Analyze theme with orchestrator context and return wrapped result"
    
    # Create log function with trace context
    log_fn = lambda event, **kw: log_analysis_event(event, trace_ctx, **kw)
    
    # Call analyze_theme
    state = await analyze_theme(
        theme=theme,
        sections_lookup=self.sections_lookup,
        hdgs=self.headings,
        semaphore=semaphore,
        log_fn=log_fn,
        prior_coverage_context=prior_coverage_context,
        max_iterations=cfg.max_iter
    )
    
    # Wrap with framework metadata
    return AnalysisResult(state, trace_ctx.framework)

source

PipelineOrchestrator.run_stage1

 PipelineOrchestrator.run_stage1 (semaphore)

Run stage 1 of the pipeline

Exported source

@patch
async def run_stage1(self:PipelineOrchestrator, semaphore):
    "Run stage 1 of the pipeline"
    tasks = []
    
    collections = [
        (self.eval_data.srf_enablers, FrameworkCat.ENABLERS, format_enabler_theme),
        (self.eval_data.srf_crosscutting_priorities, FrameworkCat.CROSSCUT, format_crosscutting_theme)
    ]

    for items, framework_cat, format_fn in collections:
        for item in items:
            trace_ctx = TraceContext(self.report_id, Stage.STAGE1, FrameworkInfo(Framework.SRF, framework_cat, item.id))
            theme = format_fn(item)
            tasks.append(self.analyze_with_context(theme, trace_ctx, semaphore))

    results = await gather(*tasks)
    for result in results: 
        self.results[Stage.STAGE1][result.framework_name][result.framework_category][result.framework_theme_id] = result

# Setup
report_id = "49d2fba781b6a7c0d94577479636ee6f"
hdgs = create_heading_dict(report)
eval_data = IOMEvalData()

# Create orchestrator
orchestrator = PipelineOrchestrator(
    report_id=report_id,
    headings=hdgs,
    get_content_fn=get_content_tool,
    eval_data=eval_data,
    verbosity=2
)

# Run stage 1
stage1_semaphore = Semaphore(cfg.semaphore)
await orchestrator.run_stage1(stage1_semaphore)

49d2fba781b6a7c0d94577479636ee6f - stage1 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage1 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage1 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage1 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage1 - Analysis Complete

covered = orchestrator.results(Stage.STAGE1, filter_type="covered")
print(f"Covered themes: {len(covered)}")
print(f"Total themes analyzed: {len(orchestrator.results(Stage.STAGE1, filter_type='all'))}")

Covered themes: 3

Total themes analyzed: 11

print(covered[:5])

[
{
'theme_covered': True,
'coverage_reasoning': 'The provided text and previous evidence summaries offer a comprehensive
understanding of the funding enabler for the JI-HoA program. It addresses whether the program received sufficient
resources, how the budget was allocated, and the impact of the top-up system. It also touches upon human resources
and security issues affecting implementation. The text explains why the program succeeded in terms of financial
resources and identifies challenges related to the top-up system and security. The information is specific,
providing budget allocation percentages and examples of how top-ups were used. Both successes (meeting objectives)
and failures (uncertainties in planning) are discussed. The causal understanding is present, explaining how funding
levels and allocation impacted program outcomes. The new content further elaborates on cost-effectiveness and
efficiency, providing specific examples of improvements in return arrangements and challenges in ensuring the
stability of partner service providers. The evaluation also acknowledges the impact of external factors like
COVID-19 and regional instability. Therefore, all critical gaps are addressed, and there is enough evidence to
write a substantive briefing.',
'gaps_identified': 'None',
'explored_sections': [
'4.4.3. Did the programme receive sufficient resources to achieve its objectives? .... page 24',
'4.4. Efficiency .... page 24'
],
'framework_name': 'SRF',
'framework_category': 'Enablers',
'framework_theme_id': '3'
},
{
'theme_covered': True,
'coverage_reasoning': "The updated text provides sufficient evidence to cover the 'Data and evidence'
theme. It highlights the Regional Data Hub's contributions to data production and capacity building, which
stakeholders explicitly appreciated. The text also mentions how the JI-HoA's research informed programming by
providing insights into returnees' needs and effective approaches. While challenges related to ownership,
sustainability, and external factors are acknowledged, the report offers concrete recommendations for enhancing
capacity building, strengthening community-based reintegration efforts, and exploring continued funding for key
activities like the Regional Data Hub. The report provides enough information to discuss achievements, challenges,
lessons learned, and recommendations, allowing for a balanced and evidence-based briefing.",
'gaps_identified': 'None',
'explored_sections': [
'4. Findings .... page 10',
'Data availability .... page 16',
'1. Introduction .... page 4',
'5. Conclusions and Recommendations .... page 27'
],
'framework_name': 'SRF',
'framework_category': 'Enablers',
'framework_theme_id': '4'
},
{
'theme_covered': True,
'coverage_reasoning': "This section provides conclusions and recommendations related to the EU-IOM Joint
Initiative for Migrant Protection and Reintegration. It highlights the program's importance in addressing the needs
of migrants and returnees facing abuse, violence, and exploitation. It also discusses the importance of
community-based reintegration projects and the program's contribution to data and research on migration trends. The
text also acknowledges the limitations of ownership and sustainability due to the complexity of the program and the
fragility of the context. Recommendations are provided for enhancing capacity-building efforts, strengthening
community-based reintegration, building partnerships with service providers, and continuing funding for key
activities. The text provides evidence of outcomes, reasons for success and failure, and lessons learned. The
recommendations are evidence-based and actionable. The gaps identified in previous iterations have been
addressed.",
'gaps_identified': 'None',
'explored_sections': [
'1. Introduction .... page 4',
'4. Findings .... page 10',
'5. Conclusions and Recommendations .... page 27'
],
'framework_name': 'SRF',
'framework_category': 'Crosscutting Priorities',
'framework_theme_id': '3'
}
]

source

get_stage1_covered_context

 get_stage1_covered_context (results:__main__.PipelineResults,
                             eval_data:evaluatr.frameworks.EvalData)

Get and format covered themes in Stage 1.

Exported source

def get_stage1_covered_context(results: PipelineResults, eval_data: EvalData) -> str:
    "Get and format covered themes in Stage 1."
    covered_themes = results(Stage.STAGE1, filter_type="covered")
    if not covered_themes: return ""
    
    context_parts = []
    for theme in covered_themes:
        if theme.framework_category == str(FrameworkCat.ENABLERS):
            theme_data = next(t for t in eval_data.srf_enablers if t.id == theme.framework_theme_id)
        elif theme.framework_category == str(FrameworkCat.CROSSCUT):
            theme_data = next(t for t in eval_data.srf_crosscutting_priorities if t.id == theme.framework_theme_id)
        
        context_parts.append(f"- **{theme.framework_category} {theme_data.id}**: {theme_data.title}")
    
    return f"### Report Preliminary Context\nThis evaluation report covers the following Strategic Results Framework themes:\n" + "\n".join(context_parts)

For instance:

print(get_stage1_covered_context(orchestrator.results, eval_data))

### Report Preliminary Context
This evaluation report covers the following Strategic Results Framework themes:
- **Enablers 3**: Funding
- **Enablers 4**: Data and evidence
- **Crosscutting Priorities 3**: Protection-centred

source

PipelineOrchestrator.run_stage2

 PipelineOrchestrator.run_stage2 (semaphore)

Run stage 2 of the pipeline - GCM objectives analysis

Exported source

@patch
async def run_stage2(self:PipelineOrchestrator, semaphore):
    "Run stage 2 of the pipeline - GCM objectives analysis"
    stage1_context = get_stage1_covered_context(self.results, self.eval_data)
    tasks = []
    
    for gcm_obj in self.eval_data.gcm_objectives_small:
        trace_ctx = TraceContext(self.report_id, Stage.STAGE2, FrameworkInfo(Framework.GCM, FrameworkCat.OBJS, gcm_obj["id"]))
        theme = format_gcm_theme(gcm_obj)
        tasks.append(self.analyze_with_context(theme, trace_ctx, semaphore, stage1_context))

    results = await gather(*tasks)
    for result in results: 
        self.results[Stage.STAGE2][result.framework_name][result.framework_category][result.framework_theme_id] = result

await orchestrator.run_stage2(Semaphore(cfg.semaphore))

49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
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49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
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49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
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49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
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49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
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49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
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49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
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49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
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49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
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49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
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49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage2 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage2 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage2 - Analysis Complete

covered = orchestrator.results(Stage.STAGE2, filter_type="covered")
print(f"Covered themes: {len(covered)}")
print(f"Total themes analyzed: {len(orchestrator.results(Stage.STAGE2, filter_type='all'))}")

Covered themes: 8

Total themes analyzed: 23

print(covered)

[
{
'theme_covered': True,
'coverage_reasoning': "The report provides sufficient evidence to assess the program's impact on
strengthening the global evidence base on migration, specifically related to GCM Objective 1. The report highlights
the Regional Data Hub's contribution to data production and capacity building, which stakeholders valued. It also
mentions the use of JI-HoA research to inform programming. The recommendations section suggests continuing support
for the Regional Data Hub and investing in impact studies. While there are some areas where more detail would be
helpful, the report provides enough information to write a substantive 2-page briefing. The outcome evidence gap
and specificity gap are addressed by the specific examples of the Regional Data Hub and the use of research for
programming. The context gap is addressed by the discussion of the challenges faced by the program. The balance gap
is addressed by the discussion of both successes and failures. The causal understanding gap is addressed by the
discussion of the reasons for the program's success and failure.",
'gaps_identified': 'None',
'explored_sections': [
'1. Introduction .... page 4',
'4. Findings .... page 10',
'5. Conclusions and Recommendations .... page 27'
],
'framework_name': 'GCM',
'framework_category': 'Objectives',
'framework_theme_id': '1'
},
{
'theme_covered': True,
'coverage_reasoning': "This section provides a comprehensive overview of the program's achievements,
challenges, and recommendations related to providing accurate and timely information to migrants. The report
highlights the program's success in addressing the needs of migrants and returnees, particularly those in
vulnerable situations. It also emphasizes the importance of continuous contact between IOM and returnees to
identify and monitor their needs. The report acknowledges the program's contributions to data availability and
research on migration trends through the Regional Data Hub, which has informed policy and programming. While the
report recognizes the challenges related to ownership and sustainability, it provides specific recommendations to
enhance stakeholder capacity and ownership, strengthen community-based reintegration, and build partnerships with
independent service providers. The report also emphasizes the need for continued funding for key activities and the
importance of extending support for longer-term integration. The report addresses the causal understanding gap by
explaining how the program's activities have led to positive outcomes. It also provides sufficient evidence to
support its claims, addressing the outcome evidence gap and specificity gap. The report acknowledges both successes
and challenges, providing a balanced perspective. Therefore, the available evidence is sufficient to write a
substantive briefing on this theme.",
'gaps_identified': 'None',
'explored_sections': [
'4. Findings .... page 10',
'Outreach and awareness .... page 19',
'4.1. Relevance .... page 10',
'4.2. Coherence .... page 13',
'4.3. Effectiveness .... page 16',
'5. Conclusions and Recommendations .... page 27'
],
'framework_name': 'GCM',
'framework_category': 'Objectives',
'framework_theme_id': '3'
},
{
'theme_covered': True,
'coverage_reasoning': "The report provides evidence of achievements, failures, and contributing factors,
allowing for a balanced assessment. It offers specific examples and data points, such as exceeding targets for
field studies and capacity building, and the increased involvement of actors in return and reintegration
assistance. It also acknowledges challenges like staff turnover, financial constraints, and COVID-19, providing a
more nuanced understanding of the program's effectiveness. The report provides sufficient information to write a
substantive 2-page briefing, addressing the key aspects of achievements, reasons for success or failure, lessons
learned, and evidence-based recommendations.",
'gaps_identified': 'None',
'explored_sections': [
'4. Findings .... page 10',
'4.2. Coherence .... page 13',
'4.1. Relevance .... page 10',
'4.3. Effectiveness .... page 16'
],
'framework_name': 'GCM',
'framework_category': 'Objectives',
'framework_theme_id': '7'
},
{
'theme_covered': True,
'coverage_reasoning': "This section provides conclusions and recommendations related to the EU-IOM Joint
Initiative for Migrant Protection and Reintegration in the Horn of Africa. It highlights the program's achievements
in supporting migrants and returnees, addressing their needs, and contributing to data availability. It also
acknowledges the challenges related to ownership and sustainability due to the complexity of the program and the
fragility of the region. The recommendations focus on enhancing stakeholder capacity, strengthening community-based
reintegration efforts, building partnerships with service providers, continuing funding for key activities,
expanding support for longer-term integration, supporting safe migration pathways, and further developing tools to
measure reintegration sustainability. The report provides evidence of life-saving support, individualized
assistance, and community-based projects. It also acknowledges the limitations and external factors affecting the
program's success. The level of detail and the inclusion of both successes and failures, along with concrete
recommendations, suggest that a substantive 2-page briefing could be written based on this section. All critical
gaps have been addressed.",
'gaps_identified': 'None',
'explored_sections': [
'1. Introduction .... page 4',
'4. Findings .... page 10',
'5. Conclusions and Recommendations .... page 27'
],
'framework_name': 'GCM',
'framework_category': 'Objectives',
'framework_theme_id': '8'
},
{
'theme_covered': True,
'coverage_reasoning': "This section provides more specific information about how the program addressed the
needs of migrants, particularly through the JI-HoA. It highlights the provision of life-saving assistance like
food, water, and shelter to migrants returning from dangerous situations. It also acknowledges that only 39% of
surveyed migrants felt their needs were fully met, attributing this to delays in AVRR processes. This addresses the
outcome evidence gap by providing specific examples of services provided and satisfaction levels. The causal
understanding gap is partially addressed by linking unmet needs to delayed AVRR. The specificity gap is also
addressed by providing concrete examples of services and survey results. While the balance gap is still present,
the information provided in this section, combined with previous sections, provides a more comprehensive picture of
the program's impact on migrants' access to basic services. The report now contains enough information to write a
substantive briefing.",
'gaps_identified': 'Balance gap',
'explored_sections': [
'4. Findings .... page 10',
'2.1. Context and design of the JI-HoA .... page 5',
'4.1.1. Relevance of programme activities for migrants, returnees, and communities .... page 10',
'4.1. Relevance .... page 10',
'4.2. Coherence .... page 13',
'4.3. Effectiveness .... page 16',
'4.1.1.1 Needs of migrants .... page 10'
],
'framework_name': 'GCM',
'framework_category': 'Objectives',
'framework_theme_id': '15'
},
{
'theme_covered': True,
'coverage_reasoning': "This section provides conclusions and recommendations that address some of the gaps
identified in previous iterations. It offers evidence of the program's impact on individuals, communities, and data
availability, as well as recommendations for enhancing stakeholder capacity, strengthening community-based
reintegration, and continuing funding for key activities. While the report acknowledges challenges related to
sustainability and external factors, it also highlights the program's achievements in providing crucial support to
migrants and returnees, creating business opportunities, and fostering social cohesion. The recommendations provide
actionable steps for future programming. The causal understanding gap is partially addressed by explaining how CBR
projects reduce the need to migrate. The specificity gap is partially addressed with examples of support. The
outcome evidence gap is addressed by mentioning the impact on individuals and communities. The balance gap is
addressed by discussing both successes and challenges. Therefore, I can write a substantive 2-page briefing.",
'gaps_identified': 'None',
'explored_sections': [
'2. Background of the JI-HoA .... page 5',
'4. Findings .... page 10',
'5. Conclusions and Recommendations .... page 27'
],
'framework_name': 'GCM',
'framework_category': 'Objectives',
'framework_theme_id': '16'
},
{
'theme_covered': True,
'coverage_reasoning': "This section provides evidence of the program's effectiveness in enhancing data
availability and stakeholder capacity for evidence-based return and reintegration procedures. It addresses the
outcome evidence gap by showing that targets for field studies, stakeholder training, and establishing
migration-related networks were surpassed. It also provides some causal understanding of why these outcomes
occurred, linking them to the development of data and data collection methodologies, combined with trainings and
dialogue. While challenges like staff turnover, financial constraints, and COVID-19 are mentioned, the report
provides sufficient information to write a substantive briefing on this theme, including achievements, reasons for
success, lessons learned, and evidence-based recommendations. The balance gap is partially addressed by
acknowledging challenges alongside successes. The specificity gap is addressed through concrete examples and
numbers.",
'gaps_identified': 'None',
'explored_sections': [
'4. Findings .... page 10',
'2.1. Context and design of the JI-HoA .... page 5',
'4.1. Relevance .... page 10',
'4.2. Coherence .... page 13',
'4.3. Effectiveness .... page 16'
],
'framework_name': 'GCM',
'framework_category': 'Objectives',
'framework_theme_id': '21'
},
{
'theme_covered': True,
'coverage_reasoning': "The additional text provides more concrete evidence of the program's impact,
particularly at the individual and community levels. It highlights the importance of the JI-HoA in addressing the
needs of migrants and returnees, creating business and employment opportunities, and contributing to data
availability. The conclusions acknowledge the challenges to ownership and sustainability but also emphasize the
program's valuable contributions. The recommendations offer specific actions to enhance capacity building,
strengthen community-based reintegration, and continue supporting safe migration pathways. While the causal link
between activities and strengthened international cooperation could be stronger, the evidence is now sufficient to
write a substantive briefing covering achievements, reasons for success/failure, lessons learned, and
recommendations.",
'gaps_identified': 'Causal understanding gap',
'explored_sections': [
'2. Background of the JI-HoA .... page 5',
'1. Introduction .... page 4',
'4. Findings .... page 10',
'5. Conclusions and Recommendations .... page 27'
],
'framework_name': 'GCM',
'framework_category': 'Objectives',
'framework_theme_id': '23'
}
]

source

get_filtered_srf_output_ids

 get_filtered_srf_output_ids (results:__main__.PipelineResults,
                              eval_data:evaluatr.frameworks.EvalData)

Get filtered SRF output IDs based on covered GCM themes.

	Type	Details
results	PipelineResults	PipelineResults
eval_data	EvalData	EvalData
Returns	list	list of SRF output IDs

Exported source

def get_filtered_srf_output_ids(
    results: PipelineResults, # PipelineResults
    eval_data: EvalData # EvalData
    ) -> list: # list of SRF output IDs
    "Get filtered SRF output IDs based on covered GCM themes."
    covered_gcm = results(Stage.STAGE2, filter_type="covered")
    srf_output_ids = set()
    
    for gcm_theme in covered_gcm:
        gcm_id = gcm_theme.framework_theme_id
        if gcm_id in eval_data.gcm_srf_lut:
            srf_output_ids.update(eval_data.gcm_srf_lut[gcm_id])
    
    return list(srf_output_ids)

For instance:

filtered_srfs = get_filtered_srf_output_ids(orchestrator.results, eval_data)

print(f'nb. of filtered srf outputs: {len(filtered_srfs)}')
print(f'first 5: {filtered_srfs[:5]}')

nb. of filtered srf outputs: 81

first 5: ['3d43', '2b22', '2b63', '2b54', '2a12']

source

get_combined_context

 get_combined_context (results:__main__.PipelineResults,
                       eval_data:evaluatr.frameworks.EvalData)

Get combined context from previous stages (1 and 2).

	Type	Details
results	PipelineResults	PipelineResults
eval_data	EvalData	EvalData
Returns	str	combined context

For instance:

combined_context = get_combined_context(orchestrator.results, eval_data)
print(combined_context)

### Report Preliminary Context
This evaluation report covers the following Strategic Results Framework themes:
- **Enablers 2**: Partnership
- **Enablers 3**: Funding
- **Enablers 4**: Data and evidence
- **Enablers 5**: Learning and Innovation
- **Crosscutting Priorities 3**: Protection-centred

### Covered GCM Objectives
- **GCM 1**: Collect and utilize accurate and disaggregated data as a basis for evidence-based policies
- **GCM 3**: Provide accurate and timely information at all stages of migration
- **GCM 5**: Enhance availability and flexibility of pathways for regular migration
- **GCM 7**: Address and reduce vulnerabilities in migration
- **GCM 8**: Save lives and establish coordinated international efforts on missing migrants
- **GCM 12**: Strengthen certainty and predictability in migration procedures for appropriate screening, assessment
and referral
- **GCM 13**: Use immigration detention only as a measure of last resort and work towards alternatives
- **GCM 14**: Enhance consular protection, assistance and cooperation throughout the migration cycle
- **GCM 15**: Provide access to basic services for migrants
- **GCM 16**: Empower migrants and societies to realize full inclusion and social cohesion
- **GCM 18**: Invest in skills development and facilitate mutual recognition of skills, qualifications and 
competences
- **GCM 21**: Cooperate in facilitating safe and dignified return and readmission, as well as sustainable 
reintegration
- **GCM 23**: Strengthen international cooperation and global partnerships for safe, orderly and regular migration

source

PipelineOrchestrator.run_stage3

 PipelineOrchestrator.run_stage3 (semaphore)

Run stage 3 of the pipeline - Targeted SRF outputs analysis

Exported source

@patch
async def run_stage3(self:PipelineOrchestrator, semaphore):
    "Run stage 3 of the pipeline - Targeted SRF outputs analysis"
    combined_context = get_combined_context(self.results, self.eval_data)
    filtered_output_ids = get_filtered_srf_output_ids(self.results, self.eval_data)
    tasks = []
    
    for output_id in filtered_output_ids:
        output_context = find_srf_output_by_id(self.eval_data, output_id)
        if output_context:
            trace_ctx = TraceContext(self.report_id, Stage.STAGE3, FrameworkInfo(Framework.SRF, FrameworkCat.OUTPUTS, output_id))
            theme = format_srf_output(output_context)
            tasks.append(self.analyze_with_context(theme, trace_ctx, semaphore, combined_context))

    results = await gather(*tasks)
    for result in results: 
        self.results[Stage.STAGE3][result.framework_name][result.framework_category][result.framework_theme_id] = result

await orchestrator.run_stage3(Semaphore(cfg.semaphore))

49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Starting Analysis
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Iteration Start
49d2fba781b6a7c0d94577479636ee6f - stage3 - Section Selected
49d2fba781b6a7c0d94577479636ee6f - stage3 - Content Summarized
49d2fba781b6a7c0d94577479636ee6f - stage3 - Evidence Evaluated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Understanding Progression
49d2fba781b6a7c0d94577479636ee6f - stage3 - State Updated
49d2fba781b6a7c0d94577479636ee6f - stage3 - Analysis Complete

n_outputs = len(orchestrator.results(Stage.STAGE3, filter_type="covered"))
print(f"Number of outputs: {n_outputs}")

Number of outputs: 84

print(orchestrator.results(Stage.STAGE3, filter_type="covered"))

Unclosed client session
client_session: <aiohttp.client.ClientSession object>

[
{
'theme_covered': True,
'coverage_reasoning': "1. Quantitative evidence: Met. 68% of migrants were satisfied with MRC services, 39%
reported MRCs met all or almost all their needs, 56% of surveyed returnees were satisfied with reintegration
assistance. 95% of community members agreed projects addressed community needs, 92% agreed projects addressed needs
of returnees. 82% of partners believed IOM's local capacity building activities were useful.\n2. Qualitative
evidence: Met. Quotes from migrants about suffering on journeys, families unable to help. Returnees noted economic
assistance restores dignity. Stakeholder from Sudan suggested returnees needed more money. Some returnees revealed
they were not consulted or received different support than they selected.\n3. Outcomes shown: Met. The program
enabled migrants to return from dangerous environments. Integrated approach to economic, social, and psychosocial
support was relevant to returnees. Economic opportunities created within the community reduce the risk of social
conflict.\n4. Critical analysis: Met. Only 39% reported that the MRCs met all or almost all their needs. Some
returnees pointed out that the overall value of the economic assistance was not enough. The microbusiness
assistance did not always correspond to the knowledge of the recipient or the local context. Gaps were found in the
correspondence of specific activities to returnees' psychosocial needs.\n5. Multiple sources: Met. Evidence appears
in multiple sections: 4.1.1.1, 4.1.1.2, 4.1.1.3, 4.1.2.1, 4.1.2.2",
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3d43'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The section provides specific numbers, such as "more
than 99% of returnees referred to state and non-state actors were assisted" and "At least 89% of
returnees...reported sufficient levels of economic self-sufficiency, social stability and psychosocial
wellbeing".\n2. Qualitative evidence: Met. The section includes quotes from FGDs with returnees, such as "I feel
that I haven\'t landed yet. I feel like I am still in Libya". It also mentions community members noting that CBR
projects contributed positively to economic and employment opportunities.\n3. Outcomes shown: Met. The section
shows outcomes related to reintegration assistance, economic self-sufficiency, social stability, and psychosocial
wellbeing. It also discusses the sustainability of reintegration as measured by the Reintegration Sustainability
Index (RSI).\n4. Critical analysis: Met. The section discusses the limitations and challenges, such as the dire
economic situation hindering economic reintegration and external factors like conflict and political instability
affecting the integrated approach.\n5. Multiple sources: Met. Evidence is drawn from interim narrative reports,
FGDs, interviews with stakeholders, and the IMPACT study, spanning multiple sections of the report.',
'gaps_identified': 'None',
'explored_sections': [
'4.1.1. Relevance of programme activities for migrants, returnees, and communities .... page 10',
'4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host
communities are better able to create living standards that address drivers of migration. .... page 20',
'Overall achievement of reintegration .... page 22'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '2b63'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The section provides quantitative data, such as "68% of
respondents were satisfied with the MRC services" and "56% of the surveyed returnees were satisfied or very
satisfied with the reintegration assistance support". Met.\n2. Qualitative evidence: The section includes quotes
and examples from migrants, returnees, and stakeholders, such as experiences of migrants in dangerous environments
and the importance of economic assistance for returnees. Met.\n3. Outcomes shown: The section discusses the impact
of the program on addressing the needs of migrants, returnees, and communities, including reintegration assistance
and economic opportunities. Met.\n4. Critical analysis: The section includes discussions of challenges,
limitations, and failures, such as the fact that only 39% of respondents reported that the MRCs met all or almost
all their needs, and gaps in post-return psychosocial support. Met.\n5. Multiple sources: The evidence is drawn
from desk research, interviews, surveys, and focus group discussions, as indicated by the footnotes. Met.',
'gaps_identified': 'None',
'explored_sections': [
'4.1.2.1 Needs of governments .... page 12',
'4.1.1. Relevance of programme activities for migrants, returnees, and communities .... page 10',
'4.3.1. Specific Objective 1: Partner countries and relevant stakeholders developed or strengthened
evidence-based return and reintegration procedures .... page 16',
'4.3.1.1 Achievement of outputs and results .... page 16',
'4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host
communities are better able to create living standards that address drivers of migration. .... page 20',
'4. Findings .... page 10'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '2b54'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The section provides specific numbers such as "JI
exceeded the targets set for the \'number of field studies, surveys and other research conducted under the
programme\' (20 instead of 19)" and "the programme exceeded the targeted number of stakeholders \'strengthened
through capacity building or operational support on reintegration\' (665 instead of 434)".\n2. Qualitative
evidence: Met. The section includes quotes from stakeholders in Djibouti and Sudan regarding data gathering
capacities and the availability of data for policymaking.\n3. Outcomes shown: Met. The section shows outcomes such
as increased data availability, strengthened capacity of stakeholders, and the development of evidence-based return
and reintegration procedures. It also mentions that "136 stakeholders reported that data produced has supported
evidence-based policies, procedures, and programme design".\n4. Critical analysis: Met. The section discusses
challenges such as staff turnover, shortage of finance and qualified staff, and the impact of COVID-19, which
prevented the JI from ensuring that new data and capacity could be used for policymaking.\n5. Multiple sources:
Met. The evidence appears in multiple sections of the report, including sections 4.3 and subsections 4.3.1,
4.3.1.1, and 4.3.1.2.',
'gaps_identified': 'None',
'explored_sections': [
'4.1.1. Relevance of programme activities for migrants, returnees, and communities .... page 10',
'4.3. Effectiveness .... page 16'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '2a12'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The report mentions that 68% of respondents were satisfied
with MRC services, and 39% reported that the MRCs met all or almost all their needs. Also, 56% of surveyed
returnees were satisfied with reintegration assistance. 95% of community members agreed that the projects addressed
community needs, while 92% agreed that the projects addressed the needs of returnees.\n2. Qualitative evidence: The
report includes quotes from migrants about their distressing experiences and the lack of support from families and
communities. It also mentions the importance of economic assistance for returnees to restore their dignity and
self-trust. Some returnees pointed out that the overall value of the economic assistance was not enough.\n3.
Outcomes shown: The report shows that the program enabled migrants to return from dangerous environments and
provided reintegration assistance. It also mentions the creation of economic opportunities within the community and
the reduction of social conflict.\n4. Critical analysis: The report discusses gaps in post-return psychosocial
support, unclear information about compensation of treatment, high cost, stigma, and low awareness about MHPSS
needs among communities. Some IPs found the active guidance of the IOM less relevant, as they perceived themselves
as having more experience and knowledge than the IOM.\n5. Multiple sources: The evidence appears in multiple
sections of the report, including sections 4.1.1.1, 4.1.1.2, 4.1.1.3, and 4.1.2.1.',
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3a31'
},
{
'theme_covered': True,
'coverage_reasoning': "1. Quantitative evidence: Met. The text provides numbers such as '15,161
beneficiaries reached', satisfaction rates (Somalia 80%, Sudan 44%, Ethiopia 57%), and '54 community-based
reintegration projects benefiting approximately 76,348 individuals'.\n2. Qualitative evidence: Met. Quotes from
returnees are included, such as 'economic support was crucial for them as they returned with nothing' and
discussions from focus groups about the insufficiency of economic support.\n3. Outcomes shown: Met. The text shows
satisfaction levels with reintegration support, the impact of economic support on starting businesses and creating
social networks, and the design of community-based projects with plausible outcomes.\n4. Critical analysis: Met.
The text discusses dissatisfaction among returnees due to insufficient economic support, challenges in Ethiopia and
Sudan due to government processes and political turmoil, and the adverse impact of devaluation in Sudan.\n5.
Multiple sources: Met. Evidence is drawn from different sections of the report, including the 'Individual and
community-based reintegration' section and 'Achievement of Specific Objective 3' section.",
'gaps_identified': 'None',
'explored_sections': [
'4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host
communities are better able to create living standards that address drivers of migration. .... page 20',
'Individual and community-based reintegration .... page 20'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '2b13'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The report mentions exceeding targets for field
studies (20 instead of 19) and stakeholders strengthened through capacity building (665 instead of 434). The number
of stakeholders involved in return and reintegration assistance increased from 25 to 180.\n2. Qualitative evidence:
Met. A Sudanese stakeholder noted that "all data needed for policymaking is now available to them, although
capacity to use this data could still be strengthened further".\n3. Outcomes shown: Met. The report indicates that
the JI has generally met targets for specific objectives and result areas, sometimes surpassing them. Data produced
has supported evidence-based policies, procedures, and program design.\n4. Critical analysis: Met. The report
acknowledges challenges such as staff turnover, financial constraints, and the COVID-19 pandemic hindering the
optimal use of new data and capacity for policymaking. It also notes that in 78% of cases, no additional budget or
resources have been allocated to stakeholders.\n5. Multiple sources: Met. Evidence is drawn from multiple sections
of the report, including sections 4.1.1, 4.3, and 4.3.1, as well as from interviews with stakeholders and IOM
staff.',
'gaps_identified': 'None',
'explored_sections': [
'4.1.1. Relevance of programme activities for migrants, returnees, and communities .... page 10',
'4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host
communities are better able to create living standards that address drivers of migration. .... page 20',
'4.3. Effectiveness .... page 16'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '2b52'
},
{
'theme_covered': True,
'coverage_reasoning': "1. Quantitative evidence: Met. The text refers to the Regional Data Hub's work in
enhancing knowledge on migration and harmonizing methodologies and indicators, implying the use of quantitative
data. It also mentions the IMPACT study measuring reintegration sustainability, which uses preset indicators. \n2.
Qualitative evidence: Met. The text includes qualitative evidence such as 'The work of the Regional Data Hub was
highly appreciated by stakeholders' and 'The evaluation found that the community-level approach to reintegration
has been crucial for the achievements of the JI-HoA'.\n3. Outcomes shown: Met. The text states that the JI-HoA has
made important contributions to the availability of data and research on migration trends in the region and that
the dissemination and subsequent use of data in decision-making can have a long-term positive impact on return and
reintegration.\n4. Critical analysis: Met. The text acknowledges that ownership and sustainability of the programme
cannot be expected after five years of implementation due to the complexity of the JI-HoA and the fragility of
existing systems in the Horn of Africa countries. It also mentions that capacity-building efforts were undermined
by political priorities and staff turnover.\n5. Multiple sources: Met. Evidence appears in the 'Introduction'
section, the 'Needs of governments' section, the 'Achievement of outputs and results' section, and the 'Conclusions
and Recommendations' section.",
'gaps_identified': 'None',
'explored_sections': [
'1. Introduction .... page 4',
'4.1.2.1 Needs of governments .... page 12',
'4.3.1. Specific Objective 1: Partner countries and relevant stakeholders developed or strengthened
evidence-based return and reintegration procedures .... page 16',
'5. Conclusions and Recommendations .... page 27'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3c52'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The report mentions that 68% of respondents were satisfied
with MRC services, and 39% reported that the MRCs met all or almost all their needs. Also, 56% of surveyed
returnees were satisfied with reintegration assistance. 95% of community members agreed that the projects addressed
community needs, while 92% agreed that the projects addressed the needs of returnees. These are specific numbers
related to the theme.\n2. Qualitative evidence: The report includes quotes from migrants and stakeholders about
their experiences with the program, such as the importance of economic assistance to enable returnees to develop
sources of income and restore their dignity. It also mentions gaps in post-return psychosocial support.\n3.
Outcomes shown: The report discusses the impact of the program on migrants, returnees, and communities, such as
addressing their needs, providing economic assistance, and reducing the drive to migrate out of economic
necessity.\n4. Critical analysis: The report includes discussions of challenges, limitations, and failures, such as
unmet needs reported by some migrants, insufficient economic assistance for returnees, and gaps in post-return
psychosocial support. It also mentions uneven community support and varying partner capacities.\n5. Multiple
sources: The evidence appears in multiple sections of the report, including findings, relevance, and coherence. It
also draws on desk research, interviews, surveys, and focus group discussions.',
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1b21'
},
{
'theme_covered': True,
'coverage_reasoning': "1. Quantitative evidence: Met. The section provides data on the number of migrants
supported to return voluntarily (9025 against a target of 8450), migrants in transit provided with protection and
direct assistance (8960 against a target of 8450), and satisfaction rates with travel arrangements (95% satisfied).
Also, reintegration assistance was provided to 15161 beneficiaries.\n2. Qualitative evidence: Met. The section
includes quotes from stakeholders in Djibouti noting the effectiveness of providing migration-related information
and from returnees stating that their return would not have been possible without IOM. Also, there are findings
from focus groups with returnees regarding satisfaction with medical, psychosocial support, and social support.\n3.
Outcomes shown: Met. The section demonstrates outcomes such as migrants being able to make informed decisions to
return (95% reporting sufficient information), safe and dignified return processes, and the establishment of
community-based reintegration projects supporting beneficiaries.\n4. Critical analysis: Met. The section discusses
challenges such as long waiting times for AVR, varying satisfaction levels with reintegration support across
countries (Somalia 80% vs. Sudan 44%), and the insufficiency of economic support for reintegration. It also
mentions factors hindering swift AVR beyond IOM's control.\n5. Multiple sources: Met. Evidence is drawn from IOM
staff interviews, stakeholder interviews, project documents, interim narrative reports, focus group discussions
with returnees, and monitoring surveys.",
'gaps_identified': 'None',
'explored_sections': [
'4.1.1. Relevance of programme activities for migrants, returnees, and communities .... page 10',
'4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along
main migration routes .... page 18'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3a52'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The report mentions exceeding targets for field studies
(20 instead of 19) and stakeholders strengthened through capacity building (665 instead of 434). It also notes that
136 stakeholders reported that data produced has supported evidence-based policies, exceeding the target of 42. The
number of actors involved in return and reintegration assistance increased from 25 to 180.\n2. Qualitative
evidence: Quotes from stakeholders in Djibouti and Sudan highlight the need for improved data gathering capacities
and the potential to strengthen data use. Examples include the Women and Social Affairs Ministry in Ethiopia using
the national returnee database.\n3. Outcomes shown: The report indicates increased availability of migration data,
increased knowledge on return and reintegration issues, and increased use of data in policymaking.\n4. Critical
analysis: The report discusses challenges such as staff turnover, financial constraints, and the impact of
COVID-19, which hinder the translation of increased data and capacity into effective policymaking. It also notes
that a significant portion of stakeholders reported no increase in budget allocations for migration issues.\n5.
Multiple sources: Evidence is drawn from the IOM logframe, stakeholder surveys, interviews with stakeholders, and
interim narrative reports, spanning multiple sections of the report.',
'gaps_identified': 'None',
'explored_sections': ['Data availability .... page 16'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3c22'
},
{
'theme_covered': True,
'coverage_reasoning': "1. Quantitative evidence: The report mentions that 68% of respondents were satisfied
with MRC services, but only 39% reported that the MRCs met all or almost all their needs. Also, 56% of surveyed
returnees were satisfied with reintegration assistance. 95% of community members agreed that the projects addressed
community needs, while 92% agreed that the projects addressed the needs of returnees.\n2. Qualitative evidence: The
report includes quotes from migrants about their experiences and the support they received. It also includes
information from focus group discussions with returnees and stakeholders.\n3. Outcomes shown: The report discusses
the impact of the program on migrants, returnees, and communities, including economic, social, and psychosocial
support. It also mentions the creation of economic opportunities within the community and the reduction of social
conflict.\n4. Critical analysis: The report identifies gaps in service provision, insufficient economic assistance,
and inadequate post-return psychosocial support. It also mentions that some partners found IOM's guidance less
relevant.\n5. Multiple sources: The evidence appears in multiple sections of the report, including the findings
section, relevance section, and coherence section.",
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1c23'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The section provides numbers such as "20 instead of
19" field studies conducted, "665 instead of 434" stakeholders strengthened, and "97% average across the four
countries" declared increased knowledge. Also, "136 stakeholders reported that data produced has supported
evidence-based policies".\n2. Qualitative evidence: Met. The section includes quotes from stakeholders in Djibouti
and Sudan regarding data gathering capacities and the availability of data for policymaking. It also mentions
examples like the Women and Social Affairs Ministry in Ethiopia using the national returnee database.\n3. Outcomes
shown: Met. The section shows outcomes such as increased data availability, strengthened stakeholder capacity, and
the use of data in policymaking. The increase in the number of stakeholders involved in return and reintegration
assistance from 25 to 180 is also a significant outcome.\n4. Critical analysis: Met. The section discusses
challenges such as staff turnover, financial constraints, and the impact of COVID-19, which hindered the optimal
use of data and capacity for policymaking. It also notes that in 78% of cases, no additional budget or resources
have been allocated.\n5. Multiple sources: Met. Evidence is drawn from the IOM logframe, stakeholder surveys,
interim narrative reports, and interviews with stakeholders from different countries, as well as project monitoring
data.',
'gaps_identified': 'None',
'explored_sections': [
'4.1.1. Relevance of programme activities for migrants, returnees, and communities .... page 10',
'4.3. Effectiveness .... page 16'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1b33'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The report mentions exceeding targets for field
studies (20 instead of 19) and stakeholders strengthened through capacity building (665 instead of 434). It also
notes an increase in stakeholders involved in return and reintegration assistance from 25 to 180.\n2. Qualitative
evidence: Met. A Sudanese stakeholder noted that "all data needed for policymaking is now available to them,
although capacity to use this data could still be strengthened further".\n3. Outcomes shown: Met. The report
indicates that data produced has supported evidence-based policies, procedures, and program design, exceeding the
original target of 42 stakeholders reporting this.\n4. Critical analysis: Met. The report discusses challenges such
as staff turnover, financial constraints, and the COVID-19 pandemic hindering the optimal use of increased data and
capacity for policymaking. It also notes that in 78% of cases, no additional budget or resources have been
allocated to stakeholders.\n5. Multiple sources: Met. Evidence is drawn from the IOM logframe, stakeholder surveys,
interim narrative reports, and interviews with stakeholders from Djibouti and Sudan.',
'gaps_identified': 'None',
'explored_sections': [
'4.1.1. Relevance of programme activities for migrants, returnees, and communities .... page 10',
'4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host
communities are better able to create living standards that address drivers of migration. .... page 20',
'4.3. Effectiveness .... page 16'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3b22'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The report mentions exceeding targets for field
studies (20 instead of 19) and stakeholders strengthened through capacity building (665 instead of 434). Also, 136
stakeholders reported that data produced has supported evidence-based policies, exceeding the target of 42. The
number of stakeholders involved in return and reintegration assistance increased from 25 to 180.\n2. Qualitative
evidence: Met. A Sudanese stakeholder noted that "all data needed for policymaking is now available to them,
although capacity to use this data could still be strengthened further".\n3. Outcomes shown: Met. The report
indicates increased data availability and strengthened stakeholder capacity, leading to the development of
evidence-based return and reintegration procedures. The increased use of data in policymaking, strategies,
processes, and plans for return and reintegration is also mentioned.\n4. Critical analysis: Met. The report
acknowledges challenges such as staff turnover, financial constraints, and the COVID-19 pandemic, which hindered
the optimal use of data and capacity for policymaking. It also notes that in 78% of cases, no additional budget or
resources have been allocated, limiting the impact of increased capacity.\n5. Multiple sources: Met. Evidence is
drawn from multiple sections of the report, including sections 4.3, 4.3.1, and references to the IOM logframe and
stakeholder surveys. Previous sections also discussed relevance and reintegration.',
'gaps_identified': 'None',
'explored_sections': [
'4.1.1. Relevance of programme activities for migrants, returnees, and communities .... page 10',
'4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host
communities are better able to create living standards that address drivers of migration. .... page 20',
'4.3. Effectiveness .... page 16'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '2b51'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The report mentions exceeding targets for field
studies (20 instead of 19) and stakeholders strengthened through capacity building (665 instead of 434). Also, 136
stakeholders reported that data produced has supported evidence-based policies, exceeding the target of 42. The
number of stakeholders involved in return and reintegration assistance increased from 25 to 180.\n2. Qualitative
evidence: Met. A Sudanese stakeholder noted that "all data needed for policymaking is now available to them,
although capacity to use this data could still be strengthened further".\n3. Outcomes shown: Met. The report
indicates that increased data availability and stakeholder capacity have contributed to the development of
evidence-based return and reintegration procedures and that data is being used in policymaking.\n4. Critical
analysis: Met. The report acknowledges challenges such as staff turnover, financial constraints, and the impact of
COVID-19, which hinder the optimal use of data and capacity for policymaking. It also notes that in 78% of cases,
no additional budget or resources have been allocated.\n5. Multiple sources: Met. Evidence is drawn from the IOM
logframe, stakeholder surveys, interim reports, and interviews with stakeholders from Djibouti and Sudan.',
'gaps_identified': 'None',
'explored_sections': [
'4.1.1. Relevance of programme activities for migrants, returnees, and communities .... page 10',
'4.3. Effectiveness .... page 16'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3b21'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The report includes statistics such as "68% of respondents
were satisfied with the MRC services" and "56% of the surveyed returnees were satisfied or very satisfied with the
reintegration assistance support". Also, "95% of community members agreed that the projects addressed community
needs, while 92% agreed that the projects addressed the needs of returnees."\n2. Qualitative evidence: The report
includes quotes from migrants and stakeholders, such as migrants sharing they "suffered on their irregular
migration journeys" and stakeholders in Djibouti stressing that "the most urgent problems were prioritised, such as
hunger, thirst, and fatigue."\n3. Outcomes shown: The report shows outcomes such as the JI-HoA enabling migrants to
return from dangerous environments and addressing the needs of returnees in terms of reintegration.\n4. Critical
analysis: The report includes challenges, limitations, or failures, such as the survey among migrants indicating
that only 39% reported that the MRCs met all or almost all their needs, and some returnees pointing out that the
overall value of the economic assistance was not enough.\n5. Multiple sources: The evidence appears in multiple
sections of the report, including sections 4.1.1.1, 4.1.1.2, and 4.1.1.3.',
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1b32'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The report mentions exceeding targets for field
studies (20 instead of 19) and stakeholders strengthened through capacity building (665 instead of 434). It also
notes that 136 stakeholders reported that data produced has supported evidence-based policies, exceeding the target
of 42. The number of actors involved in return and reintegration assistance increased from 25 to 180.\n2.
Qualitative evidence: Met. A Sudanese stakeholder noted that "all data needed for policymaking is now available to
them, although capacity to use this data could still be strengthened further".\n3. Outcomes shown: Met. The report
indicates that data produced has supported evidence-based policies, procedures, and programme design. The Women and
Social Affairs Ministry in Ethiopia has initiated a mandate to work with the national returnee database.\n4.
Critical analysis: Met. The report discusses challenges such as the need for improved data gathering capacities,
financial and staffing shortages, staff turnover, and the impact of COVID-19, which hinder the optimal use of data
and capacity for policymaking. It also notes that in 78% of cases, no additional budget or resources have been
allocated.\n5. Multiple sources: Met. Evidence is found on pages 16 and 17, and also in the figure 4.',
'gaps_identified': 'None',
'explored_sections': ['Data availability .... page 16'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3c31'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The report mentions exceeding targets for field
studies (20 instead of 19) and the number of stakeholders strengthened through capacity building (665 instead of
434). It also states that 136 stakeholders reported that data produced has supported evidence-based policies,
exceeding the target of 42. The number of actors involved in return and reintegration assistance increased from 25
to 180.\n2. Qualitative evidence: Met. Quotes from stakeholders in Djibouti and Sudan highlight the need for
improved data gathering capacities and the availability of data for policymaking, respectively.\n3. Outcomes shown:
Met. The report indicates that increased data availability has supported evidence-based policies and program
design, with examples of data use by legal entities like the Women and Social Affairs Ministry in Ethiopia.\n4.
Critical analysis: Met. The report discusses challenges such as staff turnover, financial constraints, and the
impact of COVID-19, which hindered the optimal use of data and capacity for policymaking. It also notes that a
significant percentage of stakeholders did not receive additional budget allocations for migration issues despite
increased capacity.\n5. Multiple sources: Met. Evidence is drawn from the IOM logframe, stakeholder surveys,
interviews with stakeholders from Djibouti and Sudan, and interim narrative reports.',
'gaps_identified': 'None',
'explored_sections': ['Data availability .... page 16'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1b11'
},
{
'theme_covered': True,
'coverage_reasoning': "1. Quantitative evidence: The report mentions that 68% of respondents were satisfied
with MRC services, and 39% reported that the MRCs met all or almost all their needs. Also, 56% of surveyed
returnees were satisfied with reintegration assistance. 95% of community members agreed that the projects addressed
community needs, while 92% agreed that the projects addressed the needs of returnees.\n2. Qualitative evidence: The
report includes quotes from migrants about their experiences and needs, such as suffering on irregular migration
journeys and families not being able to help. It also includes quotes from returnees about the importance of
economic assistance and the challenges they face.\n3. Outcomes shown: The report shows outcomes such as migrants
returning from dangerous environments, returnees receiving economic and psychosocial support, and communities
benefiting from projects that address their needs.\n4. Critical analysis: The report discusses challenges such as
the need for quicker service provision, gaps in post-return psychosocial support, and the fact that microbusiness
assistance did not always correspond to the knowledge of the recipient or the local context.\n5. Multiple sources:
The evidence appears in multiple sections of the report, including sections on the needs of migrants, returnees,
and communities, as well as sections on the program's relevance to stakeholders and its coherence with IOM and EU
objectives.",
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3d35'
},
{
'theme_covered': True,
'coverage_reasoning': "1. Quantitative evidence: The report mentions exceeding targets for field studies
(20 instead of 19) and stakeholders strengthened through capacity building (665 instead of 434). It also notes that
136 stakeholders reported that data produced has supported evidence-based policies, exceeding the target of 42.
Figure 4 shows the increase in actors involved in return and reintegration assistance.\n2. Qualitative evidence:
Quotes from stakeholders in Djibouti and Sudan highlight the need for improved data gathering capacities and the
potential to strengthen the use of available data. The report also mentions the Women and Social Affairs Ministry
in Ethiopia using the national returnee database.\n3. Outcomes shown: The report indicates that increased data
availability and capacity building have supported evidence-based policies, procedures, and program design. The
increased number of stakeholders involved in return and reintegration assistance also suggests a positive
outcome.\n4. Critical analysis: The report acknowledges challenges such as staff turnover, financial constraints,
and the impact of COVID-19, which hinder the optimal use of data and capacity for policymaking. It also notes that
a significant percentage of stakeholders did not receive additional budget allocations for migration issues.\n5.
Multiple sources: Evidence is drawn from the IOM logframe, stakeholder surveys, interviews with stakeholders from
Djibouti and Sudan, and interim reports. These are referenced throughout the 'Data availability' section and
'Achievement of Specific Objective 1' section.",
'gaps_identified': 'None',
'explored_sections': ['Data availability .... page 16'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3c13'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The text mentions that the JI exceeded targets for
the "number of field studies, surveys and other research conducted under the programme" (20 instead of 19). Also,
the IOM logframe shows that the programme exceeded the targeted number of stakeholders "strengthened through
capacity building or operational support on reintegration" (665 instead of 434). According to IOM\'s survey of
stakeholders, 136 stakeholders reported that data produced has supported evidence-based policies, procedures, and
programme design, which exceeds the original target of 42.\n2. Qualitative evidence: Met. Stakeholders in Djibouti
noted that additional steps still need to be taken to improve data gathering capacities. Similarly, a Sudanese
stakeholder noted that "all data needed for policymaking is now available to them, although capacity to use this
data could still be strengthened further".\n3. Outcomes shown: Met. The increased availability of migration data
was achieved mainly through the production and the publication of migration data and research outputs by the
Regional Data Hub and the RDH\'s engagement with National Statistical Offices (NSOs) and key regional migration
data stakeholders including the Intergovernmental Authority for Development (IGAD).\n4. Critical analysis: Met.
Despite the achievements, stakeholders in Djibouti noted that additional steps still need to be taken to improve
data gathering capacities. Also, some stakeholders from Sudan and Somalia noted that shortage of finance and
(qualified) staff prevent the government from actively using increased capacities for policymaking.\n5. Multiple
sources: Met. The evidence comes from the IOM logframe, stakeholder interviews, and the Regional Data Hub.',
'gaps_identified': 'None',
'explored_sections': [
'4.1.1. Relevance of programme activities for migrants, returnees, and communities .... page 10',
'Outreach and awareness .... page 19',
'4.3. Effectiveness .... page 16'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3a34'
},
{
'theme_covered': True,
'coverage_reasoning': "1. Quantitative evidence: The report mentions that 82% of partners believed IOM's
local capacity building activities were useful. 95% of community members agreed that the projects addressed
community needs, while 92% agreed that the projects addressed the needs of returnees. The new section does not add
new quantitative data.\n2. Qualitative evidence: The report includes quotes from migrants, returnees, and
stakeholders about the relevance of the program, the importance of economic assistance, and the gaps in
psychosocial support. The new section includes discussion of the importance of CBR projects and the need for
continued funding of key activities.\n3. Outcomes shown: The report shows that the program enabled migrants to
return from dangerous environments, provided economic resources for returnees, and addressed the psychosocial needs
of migrants. The new section discusses the importance of community-level reintegration and the need to extend the
scope of support to returnees with a focus on longer-term integration.\n4. Critical analysis: The report discusses
the limitations of the program, such as the need for quicker service provision, the insufficient value of economic
assistance, and the gaps in post-return psychosocial support. The new section discusses gaps found in the design,
M&E, and relevance of some CBR projects. It also mentions that governments and stakeholders do not have the
capacity to continue providing immediate, life-saving support to migrants independently.\n5. Multiple sources: The
evidence appears in multiple sections of the report, including the findings section, the recommendations section,
and the relevance section.",
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10', '5.2. Recommendations .... page 28'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3d31'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The previous section mentions "68% of respondents
were satisfied with the MRC services".\n2. Qualitative evidence: Met. The previous section includes the quote:
"Migrants shared that they suffered on their irregular migration journeys".\n3. Outcomes shown: Met. The program
enabled migrants to return from dangerous environments and provided them with food, water, clothing, and
shelter.\n4. Critical analysis: Met. The previous section mentions that "some returnees pointed out that the
overall value of the economic assistance was not enough".\n5. Multiple sources: Met. The evidence comes from
section 4 and section 4.3.1.',
'gaps_identified': 'None',
'explored_sections': [
'4. Findings .... page 10',
'4.3.1. Specific Objective 1: Partner countries and relevant stakeholders developed or strengthened
evidence-based return and reintegration procedures .... page 16'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3b32'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The report includes statistics such as "68% of respondents
were satisfied with the MRC services" and "56% of the surveyed returnees were satisfied or very satisfied with the
reintegration assistance support". Also, "95% of community members agreed that the projects addressed community
needs, while 92% agreed that the projects addressed the needs of returnees".\n2. Qualitative evidence: The report
includes quotes from migrants and stakeholders, such as migrants sharing their distressing experiences and
stakeholders highlighting the prioritization of urgent needs like hunger and thirst. Also, returnees pointed out
that the overall value of the economic assistance was not enough.\n3. Outcomes shown: The report shows outcomes
such as returnees developing sources of income through start-up businesses, communities benefiting from economic
opportunities, and the program addressing the psychosocial needs of migrants.\n4. Critical analysis: The report
discusses challenges such as the need for quicker service provision, insufficient economic assistance, gaps in
post-return psychosocial support, and instances where microbusiness assistance did not align with recipients\'
knowledge.\n5. Multiple sources: Evidence is drawn from desk research, interviews with migrants and stakeholders,
focus group discussions, satisfaction surveys, and IOM reports, spanning multiple sections of the report.',
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1c11'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. "According to the RA Monitoring and Satisfaction
surveys, 56% of the surveyed returnees were satisfied or very satisfied with the reintegration assistance support
provided by the JI-HoA." \n2. Qualitative evidence: Met. "Focus Group Discussions highlighted the importance of the
economic assistance to enable returnees to develop sources of income (e.g. through start-up businesses or
employment)." Also, "Since returnees are coming back usually \'empty-handed\', they are experiencing shame, guilt,
and are stigmatised by their communities and relatives. The economic support offered by the JI-HoA not only
provides them with resources to start their business but also restores their dignity and self-trust".\n3. Outcomes
shown: Met. The section discusses the reintegration assistance provided and its impact on returnees\' economic
stability, dignity, and mental health.\n4. Critical analysis: Met. The section mentions that "some returnees
pointed out that the overall value of the economic assistance was not enough" and that "the microbusiness
assistance did not always correspond to the knowledge of the recipient or the local context." It also notes gaps in
post-return psychosocial support.\n5. Multiple sources: Met. Evidence is drawn from RA Monitoring and Satisfaction
surveys, Focus Group Discussions, stakeholder interviews, research reports, and also from the previous sections.',
'gaps_identified': 'None',
'explored_sections': [
'1. Introduction .... page 4',
'4.1.1.2 Needs of returnees .... page 10',
'4.3.1. Specific Objective 1: Partner countries and relevant stakeholders developed or strengthened
evidence-based return and reintegration procedures .... page 16'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3a44'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The report mentions satisfaction rates with MRC services
and reintegration assistance, along with community agreement on project impact. These are specific numbers related
to the theme. 2. Qualitative evidence: The report includes quotes from migrants about their experiences and the
challenges they face, as well as feedback on the economic assistance provided. 3. Outcomes shown: The report
demonstrates how the program enabled returns, provided integrated support, and created economic opportunities, all
contributing to addressing drivers of conflict and displacement. 4. Critical analysis: The report acknowledges gaps
in service provision, insufficient economic assistance, and the need for better psychosocial support, showing a
balanced perspective. 5. Multiple sources: Evidence is drawn from sections 4.1.1.1, 4.1.1.2, 4.1.1.3, 4.1.2.2 and
4.3.3.1, indicating multiple sources of information.',
'gaps_identified': 'None',
'explored_sections': [
'4. Findings .... page 10',
'4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host
communities are better able to create living standards that address drivers of migration. .... page 20'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '2a22'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The report mentions that the JI supported 9025 migrants to
return voluntarily, exceeding the target of 8450. It also states that 8960 migrants in transit received protection
and direct assistance, surpassing the target of 8450. Additionally, 15161 beneficiaries received reintegration
assistance, exceeding the target of 12800. Satisfaction rates with travel arrangements were high, with 95% of
assisted migrants satisfied and 99.6% feeling the travel was well-organized and safe. 2. Qualitative evidence: A
stakeholder in Djibouti noted the effectiveness of the JI in providing migration-related information. Returnees
involved in Focus Groups noted specifically that "their return would not have been possible without IOM". 3.
Outcomes shown: The report demonstrates that crisis-affected populations received movement assistance through
voluntary return programs, transit assistance, and reintegration support. The high satisfaction rates with travel
arrangements and the positive feedback from returnees indicate that the assistance provided was effective in
facilitating safe and dignified returns. 4. Critical analysis: The report acknowledges challenges such as long
waiting times for AVR, which led some migrants to choose alternative return methods. It also notes that
satisfaction levels with reintegration assistance varied across countries, with dissatisfaction primarily due to
the insufficiency of economic support. The report also mentions unmet targets regarding support to institutions in
Ethiopia due to prolonged government processes. 5. Multiple sources: Evidence is found in multiple sections of the
report, including 4.3.2.1 (Outreach and awareness, Assistance to stranded migrants), 4.3.2.2 (Achievement of the
Objective), and 4.3.3.1 (Individual and community-based reintegration).',
'gaps_identified': 'None',
'explored_sections': [
'4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along
main migration routes .... page 18'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1a16'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The report includes quantitative data such as satisfaction
rates among migrants (68% satisfied with MRC services) and returnees (56% satisfied with reintegration assistance).
Also, 95% of community members agreed that the projects addressed community needs, while 92% agreed that the
projects addressed the needs of returnees.\n2. Qualitative evidence: The report provides quotes from migrants and
returnees about their experiences and needs, as well as examples of how the program addressed those needs (e.g.,
economic assistance restoring dignity). There are also quotes from stakeholders about the relevance of the
program.\n3. Outcomes shown: The report shows outcomes such as returnees starting businesses, improved mental
health, and reduced social conflict within communities.\n4. Critical analysis: The report includes discussion of
challenges, limitations, and failures, such as delays in service provision, insufficient economic assistance, and
gaps in post-return psychosocial support.\n5. Multiple sources: The evidence appears in multiple sections of the
report, including findings, relevance, and coherence, as well as in desk research, interviews, surveys, and focus
group discussions.',
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1b12'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Not explicitly present in this section, but the mention of
data collection and methodologies implies the need for quantitative data. However, no specific numbers are
provided.\n2. Qualitative evidence: The section mentions that the development of data and data collection
methodologies, combined with trainings and dialogue, will contribute to increased data availability and
strengthened capacity of stakeholders.\n3. Outcomes shown: The section states that increased data availability and
strengthened stakeholder capacity results in the development of evidence-based return and reintegration
procedures.\n4. Critical analysis: No challenges, limitations, or failures are discussed in this specific
section.\n5. Multiple sources: The evidence appears in the section 4.3.1 and 4.1.2.1, indicating different sections
of the report.',
'gaps_identified': 'No gaps identified.',
'explored_sections': [
'4.1.2.1 Needs of governments .... page 12',
'4.3.1. Specific Objective 1: Partner countries and relevant stakeholders developed or strengthened
evidence-based return and reintegration procedures .... page 16'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3a14'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The report mentions that the JI supported 9025
migrants to return voluntarily and provided 8960 migrants in transit with protection and direct assistance. Also,
95% of assisted migrants were satisfied with travel arrangements. \n2. Qualitative evidence: Met. A stakeholder in
Djibouti noted that the JI was effective in providing migration related information. Returnees involved in the
Focus Groups noted specifically that "their return would not have been possible without IOM".\n3. Outcomes shown:
Met. The report shows that the JI allowed for safe, humane, and dignified return of migrants while taking into
consideration their needs and vulnerabilities. The majority of returnees were satisfied with the support received,
which helped them to start a business or search for employment and helped them create new social networks.\n4.
Critical analysis: Met. The 2019 mid-term evaluation noted that stakeholders in both Somalia and Sudan were
concerned about the long waiting times for AVR. Also, the report mentions that whether return processes have
actually become safer, more humane and more dignified in general (without the support of IOM) is unclear.\n5.
Multiple sources: Met. Evidence is drawn from sections 4 and 4.3.2.',
'gaps_identified': 'None',
'explored_sections': [
'4. Findings .... page 10',
'4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along
main migration routes .... page 18'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3b43'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The report mentions the Regional Data Hub\'s effectiveness
in "harmonizing methodologies and indicators" for data collection. (Met)\n2. Qualitative evidence: Stakeholders
"explicitly appreciated the work of the Regional Data Hub in terms of data production and capacity building."
(Met)\n3. Outcomes shown: The report states that the JI-HoA has made important contributions to the availability of
data and research on migration trends in the region, and that the dissemination and subsequent use of data in
decision-making can have a long-term positive impact on return and reintegration. (Met)\n4. Critical analysis: The
report acknowledges that capacity-building efforts were undermined by political priorities and staff turnover.
(Met)\n5. Multiple sources: The evidence appears in sections 4.1.2.1, 4.3.1 and 5.1. (Met)',
'gaps_identified': 'None',
'explored_sections': [
'4.1.2.1 Needs of governments .... page 12',
'4.3.1. Specific Objective 1: Partner countries and relevant stakeholders developed or strengthened
evidence-based return and reintegration procedures .... page 16',
'5. Conclusions and Recommendations .... page 27'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3c43'
},
{
'theme_covered': True,
'coverage_reasoning': "1. Quantitative evidence: The report mentions that 68% of respondents were satisfied
with MRC services, but only 39% reported that the MRCs met all or almost all their needs. Also, 56% of surveyed
returnees were satisfied with reintegration assistance. 95% of community members agreed that the projects addressed
community needs, while 92% agreed that the projects addressed the needs of returnees.\n2. Qualitative evidence: The
report includes quotes from migrants about their experiences and the importance of economic assistance. It also
includes perspectives from stakeholders and implementing partners.\n3. Outcomes shown: The report discusses the
relevance of the program to the needs of migrants, returnees, communities, and stakeholders, indicating that the
program had some impact on addressing those needs. It also mentions the creation of economic opportunities within
the community reduces the risk of social conflict.\n4. Critical analysis: The report discusses gaps in service
provision, the value of economic assistance, and the integration of psychosocial support. It also mentions that
some partners found IOM's guidance less relevant.\n5. Multiple sources: The evidence is drawn from desk research,
interviews, surveys, focus group discussions, and reports from various stakeholders and IOM staff. The evidence
appears in multiple sections, including the findings on relevance and coherence.",
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1c22'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The text mentions that the JI provided reintegration
assistance to 15,161 beneficiaries, exceeding the target of 12,800. It also states that the average satisfaction
rate was 55% across Somalia, Ethiopia, and Sudan, with Somalia achieving 80% satisfaction but Sudan only 44%.
Additionally, 54 community-based reintegration projects were initiated, benefiting approximately 76,348
individuals.\n2. Qualitative evidence: Met. The text includes quotes from Focus Group Discussions (FGDs) where
returnees expressed that economic support was crucial as they returned "with nothing," helping them start
businesses or find employment and create new social networks. It also mentions that the majority of FGD
participants in Sudan were satisfied with medical, psychosocial support, and social support.\n3. Outcomes shown:
Met. The evidence shows that reintegration assistance led to returnees starting businesses, finding employment, and
creating new social networks. The community-based reintegration projects also benefited approximately 76,348
community and returnee beneficiaries.\n4. Critical analysis: Met. The text mentions that the main factors causing
dissatisfaction related to the insufficiency of economic support. Some Somalian returnees believed that the
economic support was too little, and a FGD in Sudan concluded that "the total budget allocated to the income
generation projects is not sufficient to start projects/generate income to support a family."\n5. Multiple sources:
Met. Evidence appears in both section 4 (previous turn) and the current section on Individual and community-based
reintegration.',
'gaps_identified': 'None',
'explored_sections': [
'4. Findings .... page 10',
'Individual and community-based reintegration .... page 20'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '2b31'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The text mentions the lack of harmonized migration data
across the region, implying a need for better data collection infrastructure. While not direct numbers on
infrastructure, it points to a systemic gap. (Met)\n2. Qualitative evidence: The text states that before JI-HoA,
governments had limited frameworks or mechanisms for migration, no tools, and no national capacity building
strategies. This suggests a lack of adequate infrastructure and equipment for border management. (Met)\n3. Outcomes
shown: The text indicates that the JI-HoA program addressed the gap in government capacity through capacity
building activities and tools such as SOPs and various guidelines. This shows an improvement in government
capabilities related to migration management. (Met)\n4. Critical analysis: The text acknowledges that urgent
problems such as COVID-19, security issues, and economic crises prevented governments from prioritizing return
migration, which implies that border management infrastructure and equipment may have been neglected due to these
competing priorities. (Met)\n5. Multiple sources: This section (4.1.2.1) and the previous section (4.1.2.1) both
discuss the needs of governments and the challenges they face in managing migration, indicating that the evidence
comes from multiple sections of the report. (Met)',
'gaps_identified': 'None',
'explored_sections': [
'4. Findings .... page 10',
'1. Introduction .... page 4',
'4.1.1.2 Needs of returnees .... page 10',
'4.1.1.3 Needs of community members .... page 12',
'4.1.2.1 Needs of governments .... page 12'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3b42'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The section mentions exceeding targets for field studies
(20 instead of 19) and stakeholders trained (665 instead of 434), indicating quantitative data collection and
capacity building efforts. Also, the number of stakeholders involved in return and reintegration assistance
increased from 25 to 180.\n2. Qualitative evidence: The report includes quotes from stakeholders in Djibouti and
Sudan regarding data gathering capacities and data availability for policymaking. It also mentions the Women and
Social Affairs Ministry in Ethiopia using the national returnee database.\n3. Outcomes shown: The report indicates
increased data availability, strengthened stakeholder capacity, and the use of data in policymaking, strategies,
and plans for return and reintegration. The number of stakeholders involved in return and reintegration assistance
has also increased.\n4. Critical analysis: The report acknowledges challenges such as staff turnover, financial
constraints, and the impact of COVID-19, which hindered the optimal use of data and capacity for policymaking. It
also notes that increased capacity does not always translate to actual capacity due to budget limitations.\n5.
Multiple sources: Evidence is drawn from the IOM logframe, stakeholder surveys, interim narrative reports, and
interviews with stakeholders from different countries.',
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10', '4.3. Effectiveness .... page 16'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3b44'
},
{
'theme_covered': True,
'coverage_reasoning': "1. Quantitative evidence: The report mentions that 68% of respondents were satisfied
with MRC services, and 39% reported that the MRCs met all or almost all their needs. Also, 56% of surveyed
returnees were satisfied with reintegration assistance. These are specific quantitative data points related to
health services in crisis settings.\n2. Qualitative evidence: The report includes quotes from migrants about their
experiences and the support they received, as well as discussions about the relevance of psychosocial support. For
example, returnees expressed the importance of economic assistance to restore their dignity and self-trust. The
report also mentions gaps in post-return psychosocial support.\n3. Outcomes shown: The report discusses the impact
of the JI-HoA program on returnees' reintegration, including economic, social, and psychosocial support. It also
mentions the incidence of Common Mental Disorders (CMD) among JI-HoA beneficiaries.\n4. Critical analysis: The
report includes discussions of challenges, limitations, and failures, such as the need for quicker service
provision, gaps in post-return psychosocial support, and instances where economic assistance was insufficient or
not aligned with the recipient's knowledge.\n5. Multiple sources: The evidence appears in multiple sections of the
report, including sections on relevance, coherence, and findings. It also draws on desk research, interviews,
surveys, and focus group discussions.",
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1a18'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The report mentions that the JI provided
reintegration assistance to 15,161 beneficiaries against a target of 12,800. It also states that 54 community-based
reintegration projects were initiated, benefiting approximately 76,348 individuals. Satisfaction rates are also
quantified, with Somalia at 80%, Sudan at 44%, and Ethiopia at 57%.\n2. Qualitative evidence: Met. The report
includes quotes from returnees indicating that economic support was crucial for them as they returned "with
nothing." It also mentions that the support helped them start businesses, search for employment, and create new
social networks. Dissatisfaction is also explained through quotes.\n3. Outcomes shown: Met. The report discusses
the impact of reintegration assistance on beneficiaries, including starting businesses, finding employment, and
creating social networks. It also mentions the benefits of community-based reintegration projects, focusing on
capacity building and livelihood support.\n4. Critical analysis: Met. The report acknowledges that the overall
satisfaction rate with reintegration support was 55%, below the 70% target. It also discusses the reasons for
dissatisfaction, such as insufficient economic support and the adverse impact of currency devaluation in Sudan.
Challenges in Ethiopia and Sudan hindered the deployment of new software applications due to government ownership
processes and political turmoil, respectively.\n5. Multiple sources: Met. Evidence is present in sections
"Individual and community-based reintegration" and "Achievement of Specific Objective 3".',
'gaps_identified': 'None',
'explored_sections': [
'4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host
communities are better able to create living standards that address drivers of migration. .... page 20',
'Individual and community-based reintegration .... page 20'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '2b61'
},
{
'theme_covered': True,
'coverage_reasoning': "1. Quantitative evidence: The report mentions the establishment of M&E systems to
track the needs of returnees, which should lead to reinforced data collection systems (page 20). This implies an
effort to gather quantitative data related to migration drivers.\n2. Qualitative evidence: The report states that
activities focus on support to returnees and community-based projects, aiming to contribute to the economic,
social, and psychosocial reintegration of returnees (page 20).\n3. Outcomes shown: The report indicates that the
activities aim to enhance livelihoods in the communities and contribute to sustainable reintegration in communities
that are able to create living standards that address drivers of migration (page 20).\n4. Critical analysis:
Section 4.1.2.1 (page 12) acknowledges that urgent issues like COVID-19, security concerns, and economic crises
hindered governments' ability to prioritize return migration.\n5. Multiple sources: Evidence appears in section
4.1.2.1 (page 12) and section 4.3.3 (page 20).",
'gaps_identified': 'None',
'explored_sections': [
'4.1.2.1 Needs of governments .... page 12',
'4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host
communities are better able to create living standards that address drivers of migration. .... page 20'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3c11'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The text mentions that "68% of respondents were
satisfied with the MRC services, only 39% reported that the MRCs met all or almost all their needs."\n2.
Qualitative evidence: Met. The text includes quotes and examples, such as "Migrants shared that they suffered on
their irregular migration journeys, had acutely distressing experiences" and "Stakeholders supporting migrants in
Djibouti stressed in this regard that the most urgent problems were prioritised, such as hunger, thirst, and
fatigue."\n3. Outcomes shown: Met. The text indicates that the program "enabled them to return from dangerous
environments" and "tried to save their lives by providing the necessary means to survive, such as food, water,
clothing and shelter."\n4. Critical analysis: Met. The text includes a discussion of limitations, noting that "only
39% reported that the MRCs met all or almost all their needs" and that "quicker service provision and AVRR were
among the most listed elements for improvement by the respondents."\n5. Multiple sources: Met. The evidence appears
in the desk research, interviews, and survey among migrants in MRC.',
'gaps_identified': 'None',
'explored_sections': ['4.1.1.1 Needs of migrants .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1a17'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The report mentions exceeding targets for field
studies (20 instead of 19) and the number of stakeholders strengthened through capacity building (665 instead of
434). It also notes that 136 stakeholders reported that data produced has supported evidence-based policies,
exceeding the target of 42. The number of actors involved in return and reintegration assistance increased from 25
to 180.\n2. Qualitative evidence: Met. Quotes from stakeholders in Djibouti and Sudan highlight the need for
improved data gathering capacities and the availability of data for policymaking, respectively.\n3. Outcomes shown:
Met. The report indicates that increased data availability has supported evidence-based policies and program
design, with examples of data use by legal entities like the Women and Social Affairs Ministry in Ethiopia.\n4.
Critical analysis: Met. The report discusses challenges such as staff turnover, financial constraints, and the
impact of COVID-19, which hindered the optimal use of data and capacity for policymaking. It also notes that a
significant percentage of stakeholders did not receive additional budget allocations for migration issues despite
increased capacity.\n5. Multiple sources: Met. Evidence is drawn from the IOM logframe, stakeholder surveys,
interviews with stakeholders from Djibouti and Sudan, and interim narrative reports.',
'gaps_identified': 'None',
'explored_sections': ['Data availability .... page 16'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3b31'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The report mentions that 68% of respondents were
satisfied with MRC services, and 39% reported that the MRCs met all or almost all their needs. Also, 56% of
surveyed returnees were satisfied with reintegration assistance. 95% of community members agreed that the projects
addressed community needs, while 92% agreed that the projects addressed the needs of returnees.\n2. Qualitative
evidence: Met. The report includes quotes from migrants about their experiences and needs, as well as feedback from
stakeholders and community members regarding the relevance and effectiveness of the program. For example, returnees
shared that they were coming back usually "empty-handed", they are experiencing shame, guilt, and are stigmatised
by their communities and relatives.\n3. Outcomes shown: Met. The report discusses the impact of the program on
migrants, returnees, and communities, including improved access to services, economic opportunities, and
psychosocial support. For example, the creation of economic opportunities within the community reduces the risk of
social conflict, while simultaneously decreasing the drive to migrate out of economic necessity among other
community members.\n4. Critical analysis: Met. The report acknowledges limitations and challenges, such as the need
for quicker service provision, insufficient economic assistance, and gaps in post-return psychosocial support. For
example, some returnees pointed out that the overall value of the economic assistance was not enough. Also, the
post-return psychosocial support was not well-integrated into the main documents of the JI-HoA programme.\n5.
Multiple sources: Met. Evidence is drawn from desk research, interviews with migrants and stakeholders, focus group
discussions, and surveys, as well as IOM reports and EU documents. These are cited throughout the section.',
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3c41'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The report mentions the establishment of M&E systems to
track the needs of returnees, which should lead to reinforced data collection systems (page 20). This implies the
collection and use of quantitative data related to returnees. 2. Qualitative evidence: The report discusses
activities focused on support to returnees, community-based projects, and monitoring, indicating a qualitative
understanding of the reintegration process (page 20). 3. Outcomes shown: The report states that the activities
should contribute to the economic, social, and psychosocial reintegration of returnees while simultaneously
enhancing livelihoods in the communities (page 20). 4. Critical analysis: The report acknowledges the challenges in
reintegration by focusing on activities that support economic, social, and psychosocial well-being, suggesting an
awareness of potential difficulties (page 20). 5. Multiple sources: Evidence appears in section 4.1.2.1 (page 12)
and section 4.3.3 (page 20).',
'gaps_identified': 'None',
'explored_sections': [
'4.1.2.1 Needs of governments .... page 12',
'4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host
communities are better able to create living standards that address drivers of migration. .... page 20'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '2b42'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The IOM logframe shows that the JI exceeded the targets
set for the "number of field studies, surveys and other research conducted under the programme" (20 instead of 19).
The programme exceeded the targeted number of stakeholders "strengthened through capacity building or operational
support on reintegration" (665 instead of 434). According to IOM\'s survey of stakeholders, 136 stakeholders
reported that data produced has supported evidence-based policies, procedures, and programme design, which exceeds
the original target of 42. The number of stakeholders (state and non-state) involved in return and reintegration
assistance has also increased from 25 (baseline in 2017) to 180 by the end of the project in 2022.\n2. Qualitative
evidence: A Sudanese stakeholder noted that "all data needed for policymaking is now available to them, although
capacity to use this data could still be strengthened further".\n3. Outcomes shown: The evaluation found that the
JI made substantial progress in increasing the availability of migration data in the Horn of Africa. Stakeholders
reported increased knowledge on return and reintegration issues and an increase in the number of actors involved in
return and reintegration assistance. According to IOM\'s survey of stakeholders, 136 stakeholders reported that
data produced has supported evidence-based policies, procedures, and programme design.\n4. Critical analysis:
Stakeholders in Djibouti noted that additional steps still need to be taken to improve data gathering capacities.
The 2021 stakeholder survey noted that 22% of stakeholders perceive that they now have larger financial allocations
of their institutional budget for migration issues than that of their budget prior to their engagement in the
EU-IOM Joint Initiative. Some stakeholders from Sudan and Somalia noted that shortage of finance and (qualified)
staff prevent the government from actively using increased capacities for policymaking. COVID-19 was also mentioned
as factor preventing the organization of workshops to design or validate mechanisms.\n5. Multiple sources: Evidence
appears in multiple sections, including "Data availability" on page 16 and "Achievement of Specific Objective 1" on
page 17.',
'gaps_identified': 'None',
'explored_sections': ['Data availability .... page 16'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1a31'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The IOM logframe shows that the JI exceeded the
targets set for the "number of field studies, surveys and other research conducted under the programme" (20 instead
of 19). The programme exceeded the targeted number of stakeholders "strengthened through capacity building or
operational support on reintegration" (665 instead of 434). According to IOM\'s survey of stakeholders, 136
stakeholders reported that data produced has supported evidence-based policies, procedures, and programme design,
which exceeds the original target of 42. The number of stakeholders involved in return and reintegration assistance
has also increased from 25 (baseline in 2017) to 180 by the end of the project in 2022.\n2. Qualitative evidence:
Met. A Sudanese stakeholder noted that "all data needed for policymaking is now available to them, although
capacity to use this data could still be strengthened further".\n3. Outcomes shown: Met. The increased availability
of migration data (result 1.1.) was achieved mainly through the production and the publication of migration data
and research outputs by the Regional Data Hub and the RDH\'s engagement with National Statistical Offices (NSOs)
and key regional migration data stakeholders including the Intergovernmental Authority for Development (IGAD).\n4.
Critical analysis: Met. Stakeholders in Djibouti noted that additional steps still need to be taken to improve data
gathering capacities. The 2021 stakeholder survey noted that 22% of stakeholders perceive that they now have larger
financial allocations of their institutional budget for migration issues than that of their budget prior to their
engagement in the EU-IOM Joint Initiative. Some stakeholders from Sudan and Somalia noted that shortage of finance
and (qualified) staff prevent the government from actively using increased capacities for policymaking. COVID-19
was also mentioned as factor preventing the organization of workshops to design or validate mechanisms.\n5.
Multiple sources: Met. Evidence appears in multiple sections, including "Data availability .... page 16" and
"4.3.1.2 Achievement of Specific Objective 1 .... page 17".',
'gaps_identified': 'None',
'explored_sections': ['Data availability .... page 16'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3c21'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The IOM logframe shows that the JI exceeded the
targets set for the "number of field studies, surveys and other research conducted under the programme" (20 instead
of 19). Also, the IOM logframe shows that the programme exceeded the targeted number of stakeholders "strengthened
through capacity building or operational support on reintegration" (665 instead of 434). According to IOM\'s survey
of stakeholders, 136 stakeholders reported that data produced has supported evidence-based policies, procedures,
and programme design, which exceeds the original target of 42.\n2. Qualitative evidence: Met. Stakeholders in
Djibouti noted that additional steps still need to be taken to improve data gathering capacities. Similarly, a
Sudanese stakeholder noted that "all data needed for policymaking is now available to them, although capacity to
use this data could still be strengthened further".\n3. Outcomes shown: Met. The increased availability of
migration data (result 1.1.) was achieved mainly through the production and the publication of migration data and
research outputs by the Regional Data Hub and the RDH\'s engagement with National Statistical Offices (NSOs) and
key regional migration data stakeholders including the Intergovernmental Authority for Development (IGAD).\n4.
Critical analysis: Met. This evaluation has found various examples of the increased use of data in policymaking,
strategies, processes and plans for return and reintegration. However, various challenges were found that hinder
stakeholders from optimally benefitting from increased data and capacity. For example, the contextual factors
presented in section 2.2 caused turnover of government staff, which undoes the positive results of trainings. Some
stakeholders from Sudan and Somalia noted that shortage of finance and (qualified) staff prevent the government
from actively using increased capacities for policymaking. COVID-19 was also mentioned as factor preventing the
organization of workshops to design or validate mechanisms.\n5. Multiple sources: Met. Evidence appears in sections
4.1.1, Outreach and awareness, and 4.3.',
'gaps_identified': 'None',
'explored_sections': [
'4.1.1. Relevance of programme activities for migrants, returnees, and communities .... page 10',
'Outreach and awareness .... page 19',
'4.3. Effectiveness .... page 16'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3a33'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The report includes statistics such as "68% of respondents
were satisfied with the MRC services" and "56% of the surveyed returnees were satisfied or very satisfied with the
reintegration assistance support". Also, "95% of community members agreed that the projects addressed community
needs, while 92% agreed that the projects addressed the needs of returnees."\n2. Qualitative evidence: The report
includes quotes from migrants and stakeholders, such as migrants sharing their distressing experiences and
stakeholders emphasizing the prioritization of urgent needs like hunger and thirst. Also, returnees pointed out
that the overall value of the economic assistance was not enough.\n3. Outcomes shown: The report shows outcomes
such as returnees developing income sources, restoration of dignity, and communities benefiting from economic
opportunities. It also mentions the reduction in the drive to migrate out of economic necessity.\n4. Critical
analysis: The report discusses challenges such as the need for quicker service provision, insufficient economic
assistance, gaps in psychosocial support, and instances where community needs assessments were not conducted
directly with community members.\n5. Multiple sources: The evidence is drawn from desk research, interviews with
migrants and stakeholders, focus group discussions, RA Monitoring and Satisfaction surveys, and various reports and
assessments (e.g., EU-IOM Joint Initiative Horn of Africa Mental Health and Psychosocial Support (MHPSS) Research
Report, Covid-19 Natural Experiment Report, IOM MRCs Regional Dashboard).',
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1a12'
},
{
'theme_covered': True,
'coverage_reasoning': "1. Quantitative evidence: Met. 95% of community members agreed that the projects
addressed community needs, and 92% agreed that the projects addressed the needs of returnees.\n2. Qualitative
evidence: Met. The quote 'needed to work on projects that were not based on our skills or that were more beneficial
to the government' shows the community's perspective.\n3. Outcomes shown: Met. The creation of economic
opportunities within the community reduces the risk of social conflict and decreases the drive to migrate.\n4.
Critical analysis: Met. The evaluation revealed that not all targeted communities received the same support from
the IOM. Some projects were not based on community skills or were more beneficial to the government.\n5. Multiple
sources: Met. Evidence appears in sections 4.3.3 and 4.1.1.3.",
'gaps_identified': 'None',
'explored_sections': [
'4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host
communities are better able to create living standards that address drivers of migration. .... page 20',
'4.1.1.3 Needs of community members .... page 12'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '2b12'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The text mentions that 68% of respondents were satisfied
with MRC services, and 56% of surveyed returnees were satisfied with reintegration assistance. Also, 95% of
community members agreed that the projects addressed community needs, while 92% agreed that the projects addressed
the needs of returnees.\n2. Qualitative evidence: The text includes quotes from migrants about their experiences
and the importance of economic assistance. It also includes feedback from stakeholders and implementing
partners.\n3. Outcomes shown: The text discusses the impact of the program on migrants, returnees, and communities,
including improved access to basic needs, reintegration support, and economic opportunities.\n4. Critical analysis:
The text acknowledges gaps in service provision speed, the adequacy of economic assistance, and the integration of
psychosocial support. It also mentions that some community projects were not based on community skills or
needs.\n5. Multiple sources: The evidence is drawn from desk research, interviews, surveys, focus group
discussions, and reports from various organizations (IOM, EU, IGAD, African Union).',
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1a11'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The IOM logframe shows the JI exceeded targets for field
studies (20 instead of 19) and stakeholders strengthened through capacity building (665 instead of 434).
Stakeholder surveys show 97% reported increased knowledge. 136 stakeholders reported that data produced has
supported evidence-based policies, procedures, and programme design, which exceeds the original target of 42. The
number of stakeholders involved in return and reintegration assistance has also increased from 25 (baseline in
2017) to 180 by the end of the project in 2022.\n2. Qualitative evidence: A Sudanese stakeholder noted that "all
data needed for policymaking is now available to them, although capacity to use this data could still be
strengthened further". Examples include the Women and Social Affairs Ministry in Ethiopia using the national
returnee database.\n3. Outcomes shown: Increased availability of migration data, increased knowledge on return and
reintegration issues, and increased use of data in policymaking.\n4. Critical analysis: Challenges include the need
for improved data gathering capacities, staff turnover, shortages of finance and qualified staff, and the impact of
COVID-19. 78% of stakeholders did not receive additional budget allocations.\n5. Multiple sources: Evidence appears
in \'Data availability\' and \'Achievement of Specific Objective 1\' sections, referencing IOM logframes,
stakeholder surveys, interviews, and interim reports.',
'gaps_identified': 'None',
'explored_sections': ['Data availability .... page 16'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3c32'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The text mentions that "68% of respondents were satisfied
with the MRC services, only 39% reported that the MRCs met all or almost all their needs."\n2. Qualitative
evidence: The text includes quotes from migrants who "suffered on their irregular migration journeys, had acutely
distressing experiences" and noted that "their families and communities could not help them."\n3. Outcomes shown:
The program enabled migrants to return from dangerous environments and provided food, water, clothing, and shelter,
addressing their immediate needs and improving their living conditions.\n4. Critical analysis: The text
acknowledges that only 39% of respondents felt that all or almost all their needs were met, and IOM explained this
by noting that many respondents were surveyed while still waiting for AVRR, indicating delays in service
provision.\n5. Multiple sources: The evidence comes from desk research, interviews with migrants, stakeholders in
Djibouti, and a survey among migrants in MRC, indicating multiple sources.',
'gaps_identified': 'None',
'explored_sections': ['4.1.1.1 Needs of migrants .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1a19'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The report mentions that 68% of respondents were satisfied
with MRC services, and 39% reported that the MRCs met all or almost all their needs. Also, 56% of surveyed
returnees were satisfied with reintegration assistance. 95% of community members agreed that the projects addressed
community needs, while 92% agreed that the projects addressed the needs of returnees.\n2. Qualitative evidence: The
report includes quotes from migrants about their experiences and needs, as well as from stakeholders about the
program\'s relevance. For example, migrants shared that they suffered on their irregular migration journeys and
that their families and communities could not help them. Returnees highlighted the importance of economic
assistance to develop sources of income and restore dignity. Some interviewees revealed that they "needed to work
on projects that were not based on our skills or that were more beneficial to the government".\n3. Outcomes shown:
The report shows that the program enabled migrants to return from dangerous environments and addressed the needs of
returnees in terms of reintegration. It also highlights the creation of economic opportunities within the community
and the reduction of social conflict.\n4. Critical analysis: The report discusses gaps in post-return psychosocial
support, insufficient economic assistance in some cases, and varying levels of community involvement in needs
assessments. It also mentions that some returnees found the overall value of the economic assistance was not enough
and that the microbusiness assistance did not always correspond to the knowledge of the recipient or the local
context.\n5. Multiple sources: The evidence appears in multiple sections of the report, including the findings
section, relevance section, and coherence section. It also draws on desk research, interviews, surveys, and focus
group discussions.',
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '2b21'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The section provides quantitative data, such as "68%
of respondents were satisfied with the MRC services" and "56% of the surveyed returnees were satisfied or very
satisfied with the reintegration assistance support provided by the JI-HoA". Also, "95% of community members agreed
that the projects addressed community needs, while 92% agreed that the projects addressed the needs of
returnees".\n2. Qualitative evidence: Met. The section includes quotes and examples from interviews and focus group
discussions, such as migrants sharing their distressing experiences and stakeholders highlighting the
prioritization of urgent problems like hunger and thirst. Also, returnees pointed out that the overall value of the
economic assistance was not enough.\n3. Outcomes shown: Met. The section shows outcomes such as the JI-HoA enabling
migrants to return from dangerous environments and the integrated approach to economic, social, and psychosocial
support being relevant to the challenges faced by returnees. The creation of economic opportunities within the
community reduces the risk of social conflict.\n4. Critical analysis: Met. The section includes a discussion of
challenges, limitations, and failures, such as the need for quicker service provision, the overall value of
economic assistance not being enough, and gaps in post-return psychosocial support.\n5. Multiple sources: Met.
Evidence appears in multiple sections (4.1, 4.1.1, 4.1.2.1, 4.3.1 and 4.3.1.1).',
'gaps_identified': 'None',
'explored_sections': [
'4.1. Relevance .... page 10',
'4.1.2.1 Needs of governments .... page 12',
'4.3.1. Specific Objective 1: Partner countries and relevant stakeholders developed or strengthened
evidence-based return and reintegration procedures .... page 16',
'4.3.1.1 Achievement of outputs and results .... page 16',
'4. Findings .... page 10'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3c51'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The report mentions that 68% of respondents were satisfied
with MRC services, but only 39% reported that the MRCs met all or almost all their needs. Also, 56% of surveyed
returnees were satisfied with reintegration assistance. 95% of community members agreed that the projects addressed
community needs, while 92% agreed that the projects addressed the needs of returnees.\n2. Qualitative evidence: The
report includes quotes from migrants about their experiences and needs, as well as feedback from stakeholders and
community members regarding the relevance and effectiveness of the program. For example, returnees pointed out that
the overall value of the economic assistance was not enough. Also, some interviewees revealed that they "needed to
work on projects that were not based on our skills or that were more beneficial to the government".\n3. Outcomes
shown: The report discusses the impact of the program on migrants, returnees, and communities, including
improvements in access to services, economic opportunities, and psychosocial support. For example, the creation of
economic opportunities within the community reduces the risk of social conflict.\n4. Critical analysis: The report
acknowledges challenges and limitations, such as the need for quicker service provision, gaps in post-return
psychosocial support, and instances where microbusiness assistance did not align with recipients\' knowledge. For
example, the report notes gaps in the post-return psychosocial support (e.g., lack of MHPSS service, unclear
information about compensation of the treatment, high cost, stigma, and low awareness about MHPSS needs among
communities).\n5. Multiple sources: The evidence is drawn from desk research, interviews, surveys, focus group
discussions, and program reports, demonstrating multiple sources of information.',
'gaps_identified': 'None',
'explored_sections': [
'4. Findings .... page 10',
'4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host
communities are better able to create living standards that address drivers of migration. .... page 20'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '2b53'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. 56% of surveyed returnees were satisfied with
reintegration assistance.\n2. Qualitative evidence: Met. Focus Group Discussions highlighted the importance of
economic assistance to enable returnees to develop sources of income. Returnees are coming back usually
"empty-handed", they are experiencing shame, guilt, and are stigmatised by their communities and relatives.\n3.
Outcomes shown: Met. The integrated approach to economic, social, and psychosocial support was of great relevance
to the challenges faced by returnees, enabling them to develop sources of income and restore their dignity.\n4.
Critical analysis: Met. Some returnees pointed out that the overall value of the economic assistance was not
enough. Gaps were found in the correspondence of specific activities to returnees\' psychosocial needs, with a lack
of MHPSS service, unclear information about compensation of the treatment, high cost, stigma, and low awareness
about MHPSS needs among communities.\n5. Multiple sources: Met. Evidence appears from RA Monitoring and
Satisfaction surveys, Focus Group Discussions, interviews with stakeholders, and research results presented at the
Research and Evidence Facility (REF) conference.',
'gaps_identified': 'None',
'explored_sections': [
'4.1.1.1 Needs of migrants .... page 10',
'4.1.1.2 Needs of returnees .... page 10'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3a51'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. "the survey among migrants in MRC indicated that,
while 68 % of respondents were satisfied with the MRC services, only 39 % reported that the MRCs met all or almost
all their needs". Also, "According to the community participation survey administered to 1,232 community members
(221 in Ethiopia, 745 in Somalia, and 266 in Sudan) between November 2019 and July 2022, the majority of the
respondents believed that the community projects under the EU-IOM JI-HoA addressed the needs of the community and
of returnees."\n2. Qualitative evidence: Met. "Migrants shared that they suffered on their irregular migration
journeys, had acutely distressing experiences and highlighted that their families and communities could not help
them". Also, "Focus Group Discussions highlighted the importance of the economic assistance to enable returnees to
develop sources of income (e.g. through start-up businesses or employment)."\n3. Outcomes shown: Met. "The JI-HoA
enabled them to return from dangerous environments, such as detention, where no other support was available." Also,
"The creation of economic opportunities within the community reduces the risk of social conflict (e.g. negative
attitudes to returnees who receive financial support as described above), while simultaneously decreasing the drive
to migrate out of economic necessity among other community members."\n4. Critical analysis: Met. "some returnees
pointed out that the overall value of the economic assistance was not enough." Also, "The post-return psychosocial
support was not wellintegrated into the main documents of the JI-HoA programme."\n5. Multiple sources: Met.
Evidence appears in section 4.1.1 and 4.1.2.',
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3b46'
},
{
'theme_covered': True,
'coverage_reasoning': "1. Quantitative evidence: The report mentions that 62% of the budget was allocated
to Specific Objective 3. Also, the report mentions that the per capita allocated budget of the JI-HoA can be
considered high in comparison to other initiatives in the region.\n2. Qualitative evidence: The report includes
that the financial resources were sufficient to meet the programme's objectives in terms of achieving the project
outcomes and results. With the given budget, the JI-HoA could ensure the safe and dignified return of migrants,
contribute to reintegration assistance (with minor reservations expressed by some implementing partners), and
increase the capacity of key stakeholders.\n3. Outcomes shown: The report discusses the program's impact on
migrants' return and reintegration, including economic, social, and psychosocial support. It also mentions the
creation of economic opportunities within the community and capacity building for governments.\n4. Critical
analysis: The report acknowledges gaps in service provision, the sufficiency of economic assistance, and
post-return psychosocial support. It also mentions that some partners found IOM's guidance less relevant. For
example, the microbusiness assistance did not always correspond to the knowledge of the recipient or the local
context. The top-up budgeting system created uncertainties regarding the implementation budget, which hindered
planning and budgeting. Also, security issues were the major reason for delays in Horn of Africa.\n5. Multiple
sources: Evidence is drawn from desk research, interviews with migrants and stakeholders, focus group discussions,
surveys, and program reports, spanning multiple sections of the report.",
'gaps_identified': 'None',
'explored_sections': [
'4. Findings .... page 10',
'4.1.2.1 Needs of governments .... page 12',
'4.4.3. Did the programme receive sufficient resources to achieve its objectives? .... page 24'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3c15'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The report mentions that 68% of respondents were satisfied
with MRC services, and 39% reported that the MRCs met all or almost all their needs. Also, a community
participation survey was administered to 1,232 community members. 56% of surveyed returnees were satisfied with
reintegration assistance.\n2. Qualitative evidence: The report includes quotes from migrants about their
experiences and the challenges they faced. It also discusses the importance of economic assistance in restoring
dignity and self-trust. The report also mentions gaps in post-return psychosocial support.\n3. Outcomes shown: The
report discusses the impact of the JI-HoA on migrants, returnees, and communities. It mentions that the program
helped migrants return from dangerous environments and addressed the needs of returnees in terms of reintegration.
The creation of economic opportunities within the community reduces the risk of social conflict.\n4. Critical
analysis: The report acknowledges gaps in service provision, the need for more tailored economic assistance, and
issues with the value of economic assistance. It also discusses gaps in post-return psychosocial support and the
lack of integration of MHPSS into the program.\n5. Multiple sources: The evidence is drawn from desk research,
interviews, surveys, and focus group discussions, as well as from multiple sections of the report (4.1.1.1,
4.1.1.2, 4.1.1.3, 4.1.2.1, 4.1.2.2, 4.2.1, 4.2.2).',
'gaps_identified': 'None',
'explored_sections': [
'4.1.2.1 Needs of governments .... page 12',
'4.3.1. Specific Objective 1: Partner countries and relevant stakeholders developed or strengthened
evidence-based return and reintegration procedures .... page 16',
'Overall performance score for relevance: $3.9 / 5$ <br> Robustness score for the evidence: $4.5 / 5$'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3b41'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The report mentions exceeding targets for field
studies and research (20 instead of 19) and the number of stakeholders strengthened through capacity building (665
instead of 434). It also notes an increase in stakeholders involved in return and reintegration assistance from 25
to 180.\n2. Qualitative evidence: Met. A Sudanese stakeholder noted that "all data needed for policymaking is now
available to them, although capacity to use this data could still be strengthened further".\n3. Outcomes shown:
Met. The report indicates that data produced has supported evidence-based policies, procedures, and program design,
exceeding the original target of 42.\n4. Critical analysis: Met. The report discusses challenges such as staff
turnover, financial constraints, and the impact of COVID-19, which hinder the optimal use of increased data and
capacity for policymaking.\n5. Multiple sources: Met. Evidence is drawn from the IOM logframe, stakeholder surveys,
interim reports, and interviews with stakeholders from Djibouti and Sudan.',
'gaps_identified': 'None',
'explored_sections': [
'4.1.1. Relevance of programme activities for migrants, returnees, and communities .... page 10',
'4.3. Effectiveness .... page 16'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1a15'
},
{
'theme_covered': True,
'coverage_reasoning': "1. Quantitative evidence: The section discusses data collection methodologies,
implying the potential for quantitative data related to migration policies. 2. Qualitative evidence: The section
mentions the development of evidence-based return and reintegration procedures, which is a qualitative aspect of
policy development. 3. Outcomes shown: The development of data and data collection methodologies, combined with
training and dialogue, contributes to increased data availability and strengthened capacity of stakeholders. 4.
Critical analysis: While not explicitly stated in this specific excerpt, the broader context of developing
'evidence-based' procedures suggests an awareness of previous shortcomings or areas needing improvement. 5.
Multiple sources: This evidence is found in section 4.3.1, and the previous summary was from section 4.1.2.1,
indicating multiple sources within the report.",
'gaps_identified': 'No gaps identified.',
'explored_sections': [
'4.1.2.1 Needs of governments .... page 12',
'4.3.1. Specific Objective 1: Partner countries and relevant stakeholders developed or strengthened
evidence-based return and reintegration procedures .... page 16'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3a12'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The report mentions that the JI supported 9025
migrants to return voluntarily to their countries of origin (against a target of 8450). Also, 95% of assisted
migrants were satisfied with travel arrangements made for them (exceeding the target of 70%).\n2. Qualitative
evidence: Met. A stakeholder in Djibouti noted that the JI was effective in providing migration related
information. Returnees involved in the Focus Groups noted specifically that "their return would not have been
possible without IOM".\n3. Outcomes shown: Met. The report shows that the JI allowed for safe, humane, and
dignified return of migrants while taking into consideration their needs and vulnerabilities. Also, the EU-IOM
Joint Initiative strengthened data collection, analysis, and dissemination on reintegration.\n4. Critical analysis:
Met. The 2019 mid-term evaluation noted that stakeholders in both Somalia and Sudan were concerned about the long
waiting times for AVR. Also, some returnees indicated that the economic support was crucial for them as they
returned "with nothing", indicating a need for such support.\n5. Multiple sources: Met. Evidence is drawn from
multiple sections (4.3.1 and 4.3.2) and includes data from IOM reports, stakeholder interviews, and focus group
discussions.',
'gaps_identified': 'None',
'explored_sections': [
'4.3.1. Specific Objective 1: Partner countries and relevant stakeholders developed or strengthened
evidence-based return and reintegration procedures .... page 16',
'4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along
main migration routes .... page 18'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3a41'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The report mentions exceeding targets for field
studies (20 instead of 19) and stakeholders strengthened through capacity building (665 instead of 434). Also, 136
stakeholders reported that data produced supported evidence-based policies, exceeding the target of 42. These
figures relate to the capacity to manage responses to disasters and climate hazards, as they reflect improved data
availability and stakeholder readiness. \n2. Qualitative evidence: Met. A Sudanese stakeholder noted that "all data
needed for policymaking is now available to them, although capacity to use this data could still be strengthened
further". This quote directly addresses the theme.\n3. Outcomes shown: Met. The report indicates that increased
data availability and stakeholder capacity building have supported evidence-based policies and procedures. The
increased involvement of state and non-state actors in return and reintegration assistance (increased from 25 to
180) also demonstrates a tangible outcome related to preparedness.\n4. Critical analysis: Met. The report
acknowledges challenges such as staff turnover, financial constraints, and the COVID-19 pandemic, which hindered
the optimal use of increased data and capacity for policymaking. It also notes that in 78% of cases, no additional
budget or resources have been allocated, limiting the impact of capacity building.\n5. Multiple sources: Met.
Evidence is drawn from the introduction, and effectiveness sections.',
'gaps_identified': 'None',
'explored_sections': [
'1. Introduction .... page 4',
'4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host
communities are better able to create living standards that address drivers of migration. .... page 20',
'4.3. Effectiveness .... page 16'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '2a11'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. "136 stakeholders reported that data produced has
supported evidence-based policies...which exceeds the original target of 42." Also, "The number of stakeholders
(state and non-state) involved in return and reintegration assistance has also increased from 25 (baseline in 2017)
to 180 by the end of the project in 2022."\n2. Qualitative evidence: Met. "Women and Social Affairs Ministry in
Ethiopia has initiated a mandate to work with the national returnee database."\n3. Outcomes shown: Met. The
increase in stakeholders using data for evidence-based policies and the increase in actors involved in return and
reintegration assistance demonstrate outcomes.\n4. Critical analysis: Met. The section discusses challenges such as
government staff turnover, financial and staffing shortages, and COVID-19 hindering the optimal use of data and
capacity for policymaking.\n5. Multiple sources: Met. Evidence is drawn from the IOM stakeholder survey, interviews
with stakeholders, and project monitoring data, spanning multiple sections of the report.',
'gaps_identified': 'None',
'explored_sections': [
'4.1.2.1 Needs of governments .... page 12',
'4.3.1. Specific Objective 1: Partner countries and relevant stakeholders developed or strengthened
evidence-based return and reintegration procedures .... page 16',
'4.3.1.1 Achievement of outputs and results .... page 16',
'4.3.1.2 Achievement of Specific Objective 1 .... page 17'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3a32'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The JI provided reintegration assistance to 15,161
beneficiaries, exceeding the target of 12,800. The JI aimed for 70% satisfaction with reintegration support,
achieving 80% in Somalia, but only 44% in Sudan and 57% in Ethiopia. 54 community-based reintegration projects
supported approximately 76,348 community and returnee beneficiaries.\n2. Qualitative evidence: Returnees indicated
that economic support was crucial, helping them start businesses or find employment and create new social networks.
Focus Groups in Sudan showed satisfaction with medical, psychosocial, and social support. However, some Somalian
returnees believed the economic support was too little, and a FGD in Sudan concluded that the budget for income
generation projects was insufficient.\n3. Outcomes shown: Reintegration assistance helped returnees start
businesses, find employment, and create new social networks. Community-based reintegration projects focused on
capacity building and livelihood support, benefiting communities and returnees.\n4. Critical analysis: Satisfaction
with reintegration support varied significantly across countries, with Sudan and Ethiopia falling short of the 70%
target due to insufficient economic support and the adverse impact of devaluation. The implementation of data
collection and analysis tools was delayed in Ethiopia and Sudan due to government ownership processes and political
turmoil.\n5. Multiple sources: Evidence appears in multiple sections, including the introduction to Individual and
community-based reintegration and the Achievement of Specific Objective 3 section.',
'gaps_identified': 'None',
'explored_sections': [
'4. Findings .... page 10',
'4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host
communities are better able to create living standards that address drivers of migration. .... page 20',
'5.2. Recommendations .... page 28',
'Individual and community-based reintegration .... page 20'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3d44'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The report mentions exceeding targets for field studies
(20 instead of 19) and stakeholders strengthened through capacity building (665 instead of 434). It also notes that
136 stakeholders reported that data produced has supported evidence-based policies, exceeding the target of 42. 2.
Qualitative evidence: Quotes from stakeholders in Djibouti and Sudan highlight the need for improved data gathering
capacities and the potential for strengthened data use. 3. Outcomes shown: The report indicates increased knowledge
on return and reintegration issues among stakeholders and examples of data use in policymaking. 4. Critical
analysis: The report discusses challenges such as staff turnover, financial constraints, and the impact of
COVID-19, which hinder the optimal use of data for policymaking. It also notes that a significant percentage of
stakeholders did not receive additional budget allocations for migration issues. 5. Multiple sources: Evidence is
drawn from the IOM logframe, stakeholder surveys, interviews, and interim reports, spanning multiple sections of
the report.',
'gaps_identified': 'None',
'explored_sections': ['Data availability .... page 16'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1a32'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The report mentions exceeding targets for field
studies (20 instead of 19) and stakeholders strengthened through capacity building (665 instead of 434). Also, 136
stakeholders reported that data produced has supported evidence-based policies, procedures, and programme design,
which exceeds the original target of 42. These numbers directly relate to the impact of the program. \n2.
Qualitative evidence: Met. A Sudanese stakeholder noted that "all data needed for policymaking is now available to
them, although capacity to use this data could still be strengthened further". There are examples where the
migration data is already used by legal entities (e.g., Women and Social Affairs Ministry in Ethiopia has initiated
a mandate to work with the national returnee database). These quotes and examples provide direct qualitative
evidence.\n3. Outcomes shown: Met. The report shows that increased data availability and stakeholder capacity have
led to the development of evidence-based return and reintegration procedures. The number of stakeholders involved
in return and reintegration assistance has increased from 25 to 180.\n4. Critical analysis: Met. The report
discusses challenges such as staff turnover, financial constraints, and the COVID-19 pandemic, which hindered the
optimal use of data and capacity for policymaking. It also notes that a significant portion of stakeholders
reported no additional budget allocation for migration issues despite increased capacity.\n5. Multiple sources:
Met. Evidence is drawn from the IOM logframe, stakeholder surveys, interviews with stakeholders from Djibouti and
Sudan, and interim narrative reports. These are different report sections.',
'gaps_identified': 'None',
'explored_sections': [
'4.1.1. Relevance of programme activities for migrants, returnees, and communities .... page 10',
'4.3. Effectiveness .... page 16'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3a13'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Not met. The section does not contain specific numbers,
percentages, counts, or statistics related to regional and international policy coordination mechanisms.\n2.
Qualitative evidence: Met. The section provides examples of how the JI-HoA aligns with the objectives of key
regional actors like IGAD and the African Union, and how it fostered coherence and promoted adherence to
international conventions and protocols.\n3. Outcomes shown: Met. The section mentions that the JI-HoA was aligned
with the work of IGAD mainly in two pillars: gathering and disseminating quality data on migration and capacity
building of the national governments. Also, under the JI-HoA, IGAD started discussions about new initiatives on
developing a child policy and regional AVRR framework.\n4. Critical analysis: Met. The previous section mentioned
that urgent problems such as COVID-19, security issues, and economic crises prevented governments from prioritizing
return migration throughout the implementation of the JI-HoA.\n5. Multiple sources: Met. The evidence is now from
multiple sections of the report.',
'gaps_identified': 'Quantitative evidence is missing.',
'explored_sections': [
"4.2.1. The JI-HoA's alignment with the objectives and standards of IOM, and objectives of the EU ....
page 14",
'4.1.2.1 Needs of governments .... page 12',
'4.2.2. Alignment with other initiatives .... page 14'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3c42'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: 56% of surveyed returnees were satisfied with
reintegration assistance.\n2. Qualitative evidence: Focus Group Discussions highlighted the importance of the
economic assistance to enable returnees to develop sources of income. Returnees are coming back usually
"empty-handed", they are experiencing shame, guilt, and are stigmatised by their communities and relatives. \n3.
Outcomes shown: The JI-HoA\'s integrated approach addressed returnees\' economic, social, and psychosocial needs.
The economic support offered by the JI-HoA not only provides them with resources to start their business but also
restores their dignity and self-trust.\n4. Critical analysis: Some returnees pointed out that the overall value of
the economic assistance was not enough. Gaps were found in the correspondence of specific activities to returnees\'
psychosocial needs.\n5. Multiple sources: The evidence appears in desk research, interviews, survey data, and focus
group discussions.',
'gaps_identified': 'None',
'explored_sections': [
'4.1.1.1 Needs of migrants .... page 10',
'4.1.1.2 Needs of returnees .... page 10'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1b31'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The report states that the JI supported 9025 migrants
to return voluntarily and provided 8960 migrants in transit with protection and direct assistance. While not
exclusively about GBV survivors, the mention of \'protection and direct assistance\' suggests some overlap. Also,
the text mentions that $95 % of assisted migrants were satisfied with travel arrangements made for them.\n2.
Qualitative evidence: Met. The report includes quotes from returnees involved in Focus Groups, noting specifically
that "their return would not have been possible without IOM". The report also mentions that the JI allowed for
safe, humane, and dignified return of migrants while taking into consideration their needs and vulnerabilities.\n3.
Outcomes shown: Met. The report shows that the JI effectively reached out to migrants who would otherwise not be in
a position to return home (87%). It also shows that 95% of surveyed migrants reported that they have been provided
with sufficient and useful information to take an informed decision to return.\n4. Critical analysis: Met. The
report acknowledges concerns about long waiting times for AVR and notes that voluntary return procedures still tend
to take too long, creating difficulties for returnees. It also mentions that key stakeholders still lack important
capacity to work on return independently.\n5. Multiple sources: Met. Evidence is drawn from sections 4.3.2.1 and
4.3.2.2.',
'gaps_identified': 'None',
'explored_sections': [
'4.1.1.1 Needs of migrants .... page 10',
'4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host
communities are better able to create living standards that address drivers of migration. .... page 20',
'4.3. Effectiveness .... page 16',
'Individual and community-based reintegration .... page 20',
'Assistance to stranded migrants .... page 19'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3d34'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The section provides statistics on reintegration
assistance, with 15,161 beneficiaries reached against a target of 12,800. It also includes satisfaction rates with
reintegration support across Somalia (80%), Ethiopia (57%), and Sudan (44%), based on a survey of 2,928
individuals. Additionally, 54 community-based reintegration projects supported approximately 76,348 community and
returnee beneficiaries. 2. Qualitative evidence: Met. The section includes quotes from returnees indicating that
economic support was crucial for them as they returned "with nothing." It also mentions that the support helped
them start businesses, search for employment, and create new social networks. 3. Outcomes shown: Met. The section
demonstrates that reintegration assistance helped returnees start businesses, find employment, and create new
social networks, indicating a positive impact on their recovery and resilience. The community-based reintegration
projects also contributed to capacity building and livelihood support. 4. Critical analysis: Met. The section
highlights that satisfaction rates with reintegration support varied significantly by country, with Sudan and
Ethiopia showing lower rates than Somalia. It also notes that the main factors causing dissatisfaction related to
the insufficiency of economic support and the adverse impact of devaluation and exchange rates in Sudan. 5.
Multiple sources: Met. Evidence appears in sections 4.1.1.3, 4.3.3, and the current section on Individual and
community-based reintegration.',
'gaps_identified': 'None',
'explored_sections': [
'4.1.1. Relevance of programme activities for migrants, returnees, and communities .... page 10',
'4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host
communities are better able to create living standards that address drivers of migration. .... page 20',
'4.1.1.3 Needs of community members .... page 12',
'Individual and community-based reintegration .... page 20'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '2b11'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. "According to the RA Monitoring and Satisfaction
surveys, 56% of the surveyed returnees were satisfied or very satisfied with the reintegration assistance support
provided by the JI-HoA". Also, "the survey among migrants in MRC indicated that, while 68 % of respondents were
satisfied with the MRC services, only 39 % reported that the MRCs met all or almost all their needs".\n2.
Qualitative evidence: Met. The report includes quotes from migrants and stakeholders about the relevance of the
program and the challenges they faced. For example, returnees pointed out that the overall value of the economic
assistance was not enough.\n3. Outcomes shown: Met. The report discusses the reintegration of returnees, access to
services, and the impact of the program on their lives. For example, the report mentions that the JI-HoA enabled
migrants to return from dangerous environments and provided them with the necessary means to survive.\n4. Critical
analysis: Met. The report acknowledges gaps in the program, such as the need for quicker service provision, more
substantial economic assistance, and better integration of post-return psychosocial support. It also mentions that
some returnees found the overall value of the economic assistance insufficient.\n5. Multiple sources: Met. Evidence
is drawn from desk research, interviews with stakeholders, surveys of migrants and community members, and focus
group discussions. These are cited throughout the \'Findings\' section.',
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3d41'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The section provides numerical data on the number of
field studies conducted (20 instead of 19), stakeholders strengthened through capacity building (665 instead of
434), and the number of stakeholders reporting increased knowledge (97% average). Also, 136 stakeholders reported
that data produced supported evidence-based policies, exceeding the target of 42. The number of stakeholders
involved in return and reintegration assistance increased from 25 in 2017 to 180 in 2022.\n2. Qualitative evidence:
Met. The section includes quotes from stakeholders in Djibouti and Sudan regarding data gathering capacities and
the availability of data for policymaking.\n3. Outcomes shown: Met. The section demonstrates that the program has
met targets for specific objectives and result areas, leading to increased data availability and strengthened
capacity of stakeholders. It also shows examples of increased use of data in policymaking.\n4. Critical analysis:
Met. The section discusses challenges such as staff turnover, financial constraints, and the impact of COVID-19,
which hindered stakeholders from optimally benefiting from increased data and capacity. It also notes that in 78%
of cases, no additional budget or resources have been allocated.\n5. Multiple sources: Met. Evidence is drawn from
the IOM logframe, stakeholder surveys, interim narrative reports, and interviews with stakeholders from Djibouti
and Sudan, as well as project monitoring data. This builds on the previous section.',
'gaps_identified': 'None',
'explored_sections': [
'4.1.1. Relevance of programme activities for migrants, returnees, and communities .... page 10',
'4.3. Effectiveness .... page 16'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1a21'
},
{
'theme_covered': True,
'coverage_reasoning': "1. Quantitative evidence: Met. The report mentions that 68% of respondents were
satisfied with MRC services, and 56% of surveyed returnees were satisfied with reintegration assistance. Also, 95%
of community members agreed that the projects addressed community needs, while 92% agreed that the projects
addressed the needs of returnees.\n2. Qualitative evidence: Met. The report includes quotes from migrants about
their experiences and the importance of economic assistance. It also includes feedback from returnees and
stakeholders about the program's relevance and areas for improvement.\n3. Outcomes shown: Met. The report discusses
the program's impact on migrants' well-being, returnees' reintegration, and community perceptions. It also mentions
the program's contribution to EU objectives and regional frameworks.\n4. Critical analysis: Met. The report
acknowledges gaps in service provision, the value of economic assistance, and the integration of psychosocial
support. It also mentions that some partners found IOM's guidance less relevant.\n5. Multiple sources: Met. The
evidence comes from desk research, interviews, surveys, focus group discussions, and reports from various
organizations (IOM, EU, IGAD, African Union).",
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1c21'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The report mentions exceeding targets for stakeholders
trained (665 instead of 434) and field studies conducted (20 instead of 19). It also notes that 136 stakeholders
reported data supporting evidence-based policies, exceeding the target of 42. The number of actors involved in
return and reintegration assistance increased from 25 to 180.\n2. Qualitative evidence: A Sudanese stakeholder
noted that "all data needed for policymaking is now available to them." The report also mentions examples where
migration data is used by legal entities, such as the Women and Social Affairs Ministry in Ethiopia.\n3. Outcomes
shown: The report indicates increased knowledge on return and reintegration issues among stakeholders (97% average
across four countries). It also shows an increase in the number of stakeholders involved in return and
reintegration assistance.\n4. Critical analysis: The report acknowledges challenges such as staff turnover,
financial constraints, and the impact of COVID-19, which hindered the optimal use of data and capacity for
policymaking. It also notes that in 78% of cases, no additional budget or resources have been allocated to
stakeholders.\n5. Multiple sources: Evidence is found in section 4.3, specifically subsections 4.3.1, 4.3.1.1, and
4.3.1.2. It also references the 2019 mid-term evaluation, IOM logframe, stakeholder surveys, and interim narrative
reports.',
'gaps_identified': 'None',
'explored_sections': ['4.3. Effectiveness .... page 16'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1b22'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The section provides specific numbers, such as "JI
had provided reintegration assistance to a total of 15161 beneficiaries, with an original target of 12800" and
satisfaction rates in Somalia (80%), Sudan (44%), and Ethiopia (57%). Also, "54 community-based reintegration
projects were initiated. These projects have supported approximately 76,348 community and returnee
beneficiaries."\n2. Qualitative evidence: Met. The section includes quotes from returnees, such as "the economic
support was crucial for them as they returned \'with nothing\'" and "the total budget allocated to the income
generation projects is not sufficient to start projects/generate income to support a family."\n3. Outcomes shown:
Met. The section shows outcomes such as the number of beneficiaries reached, satisfaction rates with reintegration
support, and the impact of community-based reintegration projects on capacity building and livelihood support.\n4.
Critical analysis: Met. The section discusses challenges and limitations, such as the lower-than-expected
satisfaction rates in Sudan and Ethiopia due to insufficient economic support and the adverse impact of devaluation
in Sudan. It also mentions delays in Ethiopia and Sudan due to government ownership processes and political
turmoil.\n5. Multiple sources: Met. Evidence is drawn from multiple sources, including the JI HoA Interim Narrative
Report, FGDs with returnees, the Reintegration Assistance Monitoring Survey, and Itad\'s Spot Analytical Report on
Community-based Reintegration Projects. These sources are cited throughout the section.',
'gaps_identified': 'None',
'explored_sections': [
'4.1.1. Relevance of programme activities for migrants, returnees, and communities .... page 10',
'4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host
communities are better able to create living standards that address drivers of migration. .... page 20',
'Individual and community-based reintegration .... page 20'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '2b62'
},
{
'theme_covered': True,
'coverage_reasoning': "1. Quantitative evidence: 56% of surveyed returnees were satisfied with
reintegration assistance.\n2. Qualitative evidence: Quotes from Focus Group Discussions highlight the importance of
economic assistance in enabling returnees to develop sources of income and restore their dignity. Some returnees
felt the economic assistance was insufficient.\n3. Outcomes shown: Returnees are able to develop sources of income,
restore their dignity, and address mental health challenges through economic, social, and psychosocial support. The
incidence of Common Mental Disorders (CMD) is significantly higher among JI-HoA beneficiaries.\n4. Critical
analysis: Some returnees pointed out that the overall value of the economic assistance was not enough. Gaps were
found in the correspondence of specific activities to returnees' psychosocial needs, including a lack of MHPSS
services, unclear information, high costs, stigma, and low awareness.\n5. Multiple sources: The evidence appears in
multiple sections of the report, including the RA Monitoring and Satisfaction surveys, Focus Group Discussions, and
the JI-HoA Programme's Lessons Learned from the Psychosocial Support Component Report.",
'gaps_identified': 'None',
'explored_sections': ['4.1.1.2 Needs of returnees .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3a43'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: 56% of surveyed returnees were satisfied with
reintegration assistance.\n2. Qualitative evidence: Quotes from focus group discussions highlight the importance of
economic assistance in restoring dignity and enabling income generation. Some returnees felt the economic
assistance was insufficient.\n3. Outcomes shown: Returnees were able to develop sources of income through start-up
businesses or employment. Economic support restored their dignity and self-trust.\n4. Critical analysis: Some
returnees pointed out that the overall value of the economic assistance was not enough. The microbusiness
assistance did not always correspond to the knowledge of the recipient or the local context. Gaps were found in
post-return psychosocial support.\n5. Multiple sources: Evidence appears in the section 4.1.1.2 Needs of returnees
and is supported by RA Monitoring and Satisfaction surveys, Focus Group Discussions, and research results presented
at the Research and Evidence Facility (REF) conference.',
'gaps_identified': 'None',
'explored_sections': ['4.1.1.2 Needs of returnees .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3d42'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. "According to the RA Monitoring and Satisfaction
surveys, 56% of the surveyed returnees were satisfied or very satisfied with the reintegration assistance support
provided by the JI-HoA". Also, "the majority of the respondents believed that the community projects under the
EU-IOM JI-HoA addressed the needs of the community and of returnees. Key statistics show that 95% of community
members agreed that the projects addressed community needs, while 92% agreed that the projects addressed the needs
of returnees."\n2. Qualitative evidence: Met. "Migrants shared that they suffered on their irregular migration
journeys, had acutely distressing experiences and highlighted that their families and communities could not help
them". Also, "Focus Group Discussions highlighted the importance of the economic assistance to enable returnees to
develop sources of income (e.g. through start-up businesses or employment). Since returnees are coming back usually
\\"empty-handed\\", they are experiencing shame, guilt, and are stigmatised by their communities and relatives. The
economic support offered by the JI-HoA not only provides them with resources to start their business but also
restores their dignity and self-trust".\n3. Outcomes shown: Met. The evidence shows that the JI-HoA program
provided essential support such as food, shelter, and reintegration assistance, which are outcomes related to the
well-being and stability of migrants and returnees.\n4. Critical analysis: Met. "In the Focus Group Discussions
(FGDs), some returnees pointed out that the overall value of the economic assistance was not enough." Also, "The
JI-HoA Programme\'s Lessons Learned from the Psychosocial Support Component Report noted gaps in the post-return
psychosocial support (e.g., lack of MHPSS service, unclear information about compensation of the treatment, high
cost, stigma, and low awareness about MHPSS needs among communities)".\n5. Multiple sources: Met. The evidence is
drawn from findings, relevance, and coherence sections.',
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '2b32'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The report states that the JI supported 9025 migrants
to return voluntarily, exceeding the target of 8450. Also, 95% of assisted migrants were satisfied with travel
arrangements. The JI provided reintegration assistance to 15161 beneficiaries, exceeding the target of 12800.
However, the JI reached an average satisfaction rate of 55% across the three countries of origin, while the target
was 70%.\n2. Qualitative evidence: Met. A stakeholder in Djibouti noted that the JI was effective in providing
migration-related information. Returnees involved in Focus Groups noted specifically that "their return would not
have been possible without IOM".\n3. Outcomes shown: Met. The report shows that the JI allowed for safe, humane,
and dignified return of migrants while taking into consideration their needs and vulnerabilities. The support
helped them to start a business or search for employment and helped them create new social networks.\n4. Critical
analysis: Met. The 2019 mid-term evaluation noted that stakeholders in both Somalia and Sudan were concerned about
the long waiting times for AVR. Focus Groups with returnees demonstrate that the main factors causing
dissatisfaction related to the insufficiency of economic support.\n5. Multiple sources: Met. The evidence comes
from IOM\'s logframe, interviews with stakeholders, desk review of project documents, Focus Groups with returnees,
and project monitoring data.',
'gaps_identified': 'None',
'explored_sections': [
'4. Findings .... page 10',
'4.3.2. Specific Objective 2: Safe, humane, dignified voluntary return processes are enhanced along
main migration routes .... page 18'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '2c11'
},
{
'theme_covered': True,
'coverage_reasoning': "1. Quantitative evidence: The report mentions that 68% of respondents were satisfied
with MRC services, and 39% reported that the MRCs met all or almost all their needs. Also, 56% of surveyed
returnees were satisfied with reintegration assistance. 95% of community members agreed that the projects addressed
community needs, while 92% agreed that the projects addressed the needs of returnees.\n2. Qualitative evidence: The
report includes quotes from migrants about their experiences and the support they received. It also includes
examples of how economic assistance restored dignity and enabled income generation for returnees. Some returnees
pointed out that the overall value of the economic assistance was not enough.\n3. Outcomes shown: The report shows
that the program enabled migrants to return from dangerous environments and provided reintegration support for
returnees. It also shows that the program addressed the needs of communities in terms of reintegration and
livelihoods support.\n4. Critical analysis: The report discusses gaps in the level of satisfaction with MRC
services, the sufficiency and relevance of economic assistance, and the integration of post-return psychosocial
support. Some partners also found IOM's guidance less relevant due to their own experience. The report also
mentions that some returnees were not consulted or received different support than they selected.\n5. Multiple
sources: The evidence appears in multiple sections of the report, including the findings section and the relevance
section.",
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3d33'
},
{
'theme_covered': True,
'coverage_reasoning': "1. Quantitative evidence: Met. 82% of partners found IOM's local capacity building
activities useful.\n2. Qualitative evidence: Met. Some IPs found IOM's guidance less relevant because they
perceived themselves as having more experience.\n3. Outcomes shown: Met. The capacity building activities aimed to
mitigate differences in technical capacity and experience among implementing partners, which is an outcome of
improved partner capacity.\n4. Critical analysis: Met. Some experienced partners found IOM's guidance less relevant
or deemed the capacity building unnecessary due to their existing capabilities.\n5. Multiple sources: Met. The
evidence comes from a survey of partners (Partnership Analysis assessment) and interview analysis.",
'gaps_identified': 'None',
'explored_sections': ['4.1.2.2 Needs of other stakeholders .... page 13'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '1a22'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The text mentions that "68% of respondents were
satisfied with the MRC services" and "56% of the surveyed returnees were satisfied or very satisfied with the
reintegration assistance support provided by the JI-HoA". Also, "95% of community members agreed that the projects
addressed community needs, while 92% agreed that the projects addressed the needs of returnees".\n2. Qualitative
evidence: Met. The text includes quotes and examples from migrants, returnees, and stakeholders about their
experiences with the program, the challenges they faced, and the support they received.\n3. Outcomes shown: Met.
The text discusses the impact of the program on migrants\' ability to return from dangerous environments, the
reintegration of returnees, and the creation of economic opportunities within communities.\n4. Critical analysis:
Met. The text discusses gaps in service provision, insufficient economic assistance, inadequate post-return
psychosocial support, and instances where the microbusiness assistance did not correspond to the knowledge of the
recipient or the local context.\n5. Multiple sources: Met. The evidence is drawn from desk research, interviews,
surveys, focus group discussions, and reports from various organizations (IOM, EU, IGAD, African Union).',
'gaps_identified': 'None',
'explored_sections': ['4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '3a42'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: Met. The report mentions exceeding targets for field
studies (20 instead of 19) and stakeholders strengthened through capacity building (665 instead of 434). Also, 136
stakeholders reported that data produced has supported evidence-based policies, exceeding the original target of
42. The number of stakeholders involved in return and reintegration assistance increased from 25 to 180.\n2.
Qualitative evidence: Met. The report includes examples of how migration data is used by legal entities, such as
the Women and Social Affairs Ministry in Ethiopia. It also mentions increased knowledge on return and reintegration
issues reported by stakeholders.\n3. Outcomes shown: Met. The report indicates that increased data availability and
stakeholder capacity have led to the development of evidence-based return and reintegration procedures and
policies. The increased number of actors involved in reintegration assistance also suggests a positive outcome.\n4.
Critical analysis: Met. The report discusses challenges such as staff turnover, financial constraints, and the
impact of COVID-19, which hindered the optimal use of data and capacity for policymaking. It also notes that in 78%
of cases, no additional budget or resources have been allocated, limiting the impact of capacity building.\n5.
Multiple sources: Met. Evidence is drawn from multiple sections of the report, including sections 4.3, 4.3.1,
4.3.1.1, and 4.3.1.2, as well as references to the mid-term evaluation and IOM logframe.',
'gaps_identified': 'None',
'explored_sections': [
'4.3.3. Specific Objective 3: Returnees are sustainably integrated in host communities, and host
communities are better able to create living standards that address drivers of migration. .... page 20',
'4.3. Effectiveness .... page 16'
],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '2a21'
},
{
'theme_covered': True,
'coverage_reasoning': '1. Quantitative evidence: The report mentions that 68% of respondents were satisfied
with MRC services, and 39% reported that the MRCs met all or almost all their needs. Also, 56% of surveyed
returnees were satisfied with reintegration assistance. 95% of community members agreed that the projects addressed
community needs, while 92% agreed that the projects addressed the needs of returnees. These are specific
quantitative data points related to the theme.\n2. Qualitative evidence: The report includes quotes from migrants
about their distressing experiences and the lack of support from families and communities. It also includes
feedback from returnees about the importance of economic assistance and the challenges they face, such as stigma
and exclusion. There are also quotes from community members and stakeholders about the relevance and effectiveness
of the JI-HoA projects.\n3. Outcomes shown: The report indicates that the JI-HoA enabled migrants to return from
dangerous environments and provided reintegration assistance to returnees. It also mentions the creation of
economic opportunities within the community, which reduces the risk of social conflict and decreases the drive to
migrate out of economic necessity. The report also mentions capacity building activities and tools such as the SOPs
and various guidelines.\n4. Critical analysis: The report discusses challenges such as unmet needs due to delayed
service provision, insufficient economic assistance, and gaps in post-return psychosocial support. It also mentions
that some returnees felt the economic assistance was not enough and that the microbusiness assistance did not
always correspond to their knowledge or the local context. The report also notes gaps in post-return psychosocial
support and unclear monitoring of MHPSS interventions. Some IPs found the active guidance of the IOM less
relevant.\n5. Multiple sources: The evidence is derived from desk research, interviews, surveys, focus group
discussions, interim narrative reports, and IOM regional dashboards, indicating multiple sources of information.',
'gaps_identified': 'None',
'explored_sections': ['4.1.2.1 Needs of governments .... page 12', '4. Findings .... page 10'],
'framework_name': 'SRF',
'framework_category': 'Outputs',
'framework_theme_id': '2c12'
}
]