Faut-il contrôler les aspects éthiques de la recherche en sciences sociales, et comment ?
Bibliographic record
Abstract
Résumé Aux États-Unis et au Canada, des sociologues, des anthropologues et des historiens, se plaignent que les comités d’éthique mettent des obstacles à leur travail de recherche. Ces comités justifient leur action par leur volonté de s’assurer que les recherches ne nuisent pas aux personnes qui fournissent des informations aux chercheurs. En France, ce contrôle n’existe pas, à quelques exceptions près. Certains chercheurs pensent qu’il n’a pas lieu d’être car ils n’ont pas de capacité de nuisance vis-à-vis des personnes qui se prêtent à leurs recherches. Mais est-ce si sûr ? Et si les comités d’éthique ne sont pas un bon mode de régulation, faut-il continuer à faire confiance à la seule capacité des chercheurs français à établir leurs propres règles ?
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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 | 0.161 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.097 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".