Optimal Level of Participatory Approach in an NGO Development Project
Bibliographic record
Abstract
Many authors, Bradley (2006), Banerjee (2007), Mohan (2008) and Sen (1999) among others, argue that the participatory approach in the development projects of a non-governmental organization, NGO, is more effective and sustainable than the externally imposed expert-driven approach. According to this research stream, the participatory approach promotes self-respect, dignity, inclusiveness, and empowerment of people involved in the project and, simultaneously, it improves the external local environment for the NGO. The key point of this paper is that adopting only the participatory approach may not be optimal, as this approach involves costs to learn about local culture, values and attitudes, and to design and implement feasible participatory development practices. Accordingly, an economically sensible and sustainable strategy for the NGO will be to use a mixture of both approaches. In this paper, the optimal level of participatory approach is theoretically derived and numerically illustrated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".