Community-Driven Development in Conflict-Affected Contexts: Revisiting Concepts, Functions and Fundamentals
Bibliographic record
Abstract
Community-Driven Development (CDD) is a popular aid delivery strategy in conflict-affected contexts. While the strategy remains appealing, the growing body of evidence suggests that CDD does not systematically deliver on all the desired outcomes. This may potentially be explained by the lack of clarity around the objectives and theoretical underpinnings of CDD interventions. This paper proposes ways to clarify the objectives, outcomes, theories of change and core processes of the CDD strategy in an effort to improve the design and evaluation of CDD interventions. We suggest schemas for prioritizing the function and outcomes of a given intervention, provide examples of reduced form theories of change and identify a set of ‘core processes.’ We hope these suggestions will assist practitioners in making the theoretical motivations, assumptions and trade-offs of their design choices that much more explicit and in so doing, improve our ability to deliver better interventions to conflict-affected populations. This paper forms part of a wider conceptual project supported by UK Department for International Development (DFID)’s Research and Evidence Division.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.043 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".