Dérives et effets pervers de l’évaluation quantitative de la recherche : sur les mauvais usages de la bibliométrie
Bibliographic record
Abstract
L’évaluation quantitative de la recherche scientifique se fonde de plus en plus sur des indicateurs bibliométriques de publications et de citations. Nous présentons ici les problèmes soulevés par l’usage simpliste de ces méthodes et rappelons les dangers d’utiliser des indicateurs et des classements mal construits et techniquement défectueux qui ne mesurent pas les dimensions qu’ils sont supposés mesurer, par exemple la « qualité » d’une publication ou d’une institution. Nous montrons que les revues francophones sont particulièrement susceptibles de souffrir des mauvais usages des classements bibliométriques trop simplistes des revues scientifiques.
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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 | 0.187 | 0.582 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".