Funds for Peace? Examining the Transformative Potential of Social Funds
Bibliographic record
Abstract
Social funds and large-scale community driven development (CDD) programmes are a popular policy instrument in post-conflict situations. This is partly because they are seen to alleviate pressure on governments to deliver development and reconstruction outcomes by transferring resources and responsibilities to community actors. However, part of their popularity can also be explained by claims that social funds and CDD programmes have the (transformative) potential to generate impacts beyond meeting basic needs, such as creating more peaceful societies at the local level and promoting trust in government. Drawing on a rigorous, evidence focused literature review, which began with researchers following a formal systematic review protocol, this practice note assesses the performance of 13 programmes against three distinct sets of impact indicators: (i) incomes, enterprise and access to services; (ii) social cohesion, stability and violence; and (iii) state-society relations. It is concluded that, although our understanding of the effectiveness of social funds and CDD in conflict-affected environments is limited by a low number of rigorous evaluations across a diverse range of contexts, as well as by an insufficient investigation of the relevant causal mechanisms, the findings so far suggest cause for cautious optimism.
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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 | 0.032 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".