ASSESSING THE EVIDENCE BASE FOR USING CONFLICT-SENSITIVE APPROACHES TO IMPROVE DEVELOPMENT OUTCOMES IMPACTING YOUTH AND CHILDREN: A SYSTEMATIC REVIEW OF THE RESEARCH
Bibliographic record
Abstract
This article examines the evidence base for using conflict-sensitive approaches to improve development outcomes in human-serving sectors. The study employed a systematic review methodology to identify and analyze evidence from a corpus of 49 studies that met inclusion criteria from a universe of 571 papers. Results from this review indicate that the evidence base is underdeveloped for demonstrating outcomes from integrating conflict-sensitive practices within human-serving sectors. We find that the evidence gaps may be the result of inconsistencies among the definitions and methods of measurement for conflict-sensitive practice and subsequent evaluations of such practices. Evidence from our review suggests that the education sector has developed the largest number of concrete conflict-sensitive tools and practices that can be formally evaluated for impact on outcomes and brought to scale for the benefit of the sector and the broader development community.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.076 | 0.298 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".