Défis liés à la mise en place des recommandations de la Commission Charbonneau
Bibliographic record
Abstract
Huit mois après le dépôt du rapport de la Commission Charbonneau, nous avons souhaité explorer les défis liés à la mise en place des recommandations.Avec un regard constructif, nous nous questionnons sur le fait de savoir à qui incombe le suivi d’un rapport de cette envergure ainsi que les obligations légales liées à cette question et nous nous permettons d’émettre certaines idées liées à une vision globale en matière de reddition de comptes. Tout en partageant nos réflexions inspirées des bonnes pratiques en matière de gestion du risque de fraude, nous présentons la complexité de la mise en place d’une recommandation tels le soutien et la protection des lanceurs d’alerte.
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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 | 0.146 | 0.308 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.031 | 0.013 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.022 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 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".