L’expérience d’une démarche pluraliste dans un pays en guerre: L’Afghanistan
Bibliographic record
Abstract
Abstract: Participation is often essential for successfully appropriating the results of an evaluation. Afghanistan has been a country at war since 1979, and its successive governments have left the entire public health system in the hands of international aid agencies. Having experienced and evaluated an implementation of financing mechanisms for health services, we analyze how, in a context of international emergency aid, a pluralist approach was possible, and observe the appropriation of results. The objective of this article is to identify facilitating factors of such an approach. To do this, we use an analytical framework based on and adapted from Patton’s definition of participatory evaluation, which entails three main categories: content, process, and aims of the evaluation. We think that reinforcing the links engaging evaluator and participants through participatory evaluation can partially counter the potentially negative effects of a context involving a country at war.
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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 | 0.047 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.058 | 0.023 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".