Utilisation de données secondaires et signature scientifique lors de l'évaluation d'une intervention en santé mondiale
Bibliographic record
Abstract
Dans le domaine de la santé mondiale, les bailleurs de fonds internationaux financent de nombreuses interventions dont ils souhaitent l'efficacité. Ils financent ainsi parfois des évaluations externes, le plus souvent menées par des chercheurs du Nord, pour en faire la démonstration. En outre, il existe de multiples bases de données, souvent collectées par les chercheurs du Sud, utiles pour réaliser ce type d'études. Mais cette multiplicité d'acteurs, de collaboration, d'enjeux et de potentiels conflits d'intérêts pose des défis importants sur le plan de l'utilisation de données secondaires et de la signature scientifiques des publications qui peuvent en découler. Cette étude de cas propose une réflexion à cet égard.
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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 | 0.596 | 0.781 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.036 | 0.020 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".