Institution Supérieure de Contrôle et lutte contre la corruption : Mandat ou Contribution ? Cas de la France, du Québec et du Sénégal
Bibliographic record
Abstract
Résumé Alors que la lutte contre la corruption est généralement considérée comme une responsabilité de la Justice ou des agences anticorruptions, des recherches montrent que les institutions supérieures de contrôle des finances publiques (ISC) peuvent aussi jouer un rôle essentiel. Cependant, ce rôle n'est pas toujours clairement défini et pourrait diverger selon les contextes. L'objectif de cet article est de mieux cerner le rôle des ISC dans la lutte contre la corruption du point de vue des acteurs directement concernés, soit les membres des ISC. Treize entretiens, réalisés avec des vérificateurs au Québec et des magistrats en France et au Sénégal, démontrent une contribution effective de ces institutions dans le combat contre la corruption, bien qu'elles n'en aient pas le mandat explicite.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".