Participation and Poverty Reduction: An Analytical Framework and Overview of the Issues
Bibliographic record
Abstract
This paper examines the relationship between community participation and the efficacy of interventions designed to reduce poverty. It outlines a simple model that identifies three actors involved in the provision of antipoverty interventions: financiers, providers and beneficiaries. This model is used to illustrate what happens when the poor move from being passive beneficiaries to being the providers of these interventions. Beneficiary participation has the potential to lower the cost of providing these interventions. It can ensure that they more closely reflect the preferences of the population that they are designed to serve. However, this benefit is contingent on the ability of communities to engage in collective actions. In fractionalised communities, or where trust and/or social capital are weak, there is a risk that community participation may result in the capture of benefits by local elites, to the detriment of the poor. Further, we argue that the failure to delegate true decision‐making authority (allowing for de jure but not de facto participation), may result in beneficiaries being reluctant to act because of concerns that they will be subsequently overruled.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
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".