Peut-on satisfaire les exigences en matière d’appréciation des risques dans l’évaluation éthique d’un protocole de recherche impliquant des êtres humains ?
Bibliographic record
Abstract
L’évaluation des risques est une étape incontournable pour l’approbation d’un protocole de recherche impliquant des êtres humains. Toutefois, cette évaluation est très difficile et beaucoup de spécialistes croient que les sujets sont insuffisamment protégés contre les expériences éthiquement inacceptables. Il est possible que les difficultés rencontrées lors de cette évaluation proviennent d’une mauvaise définition du concept de « risque », cette définition ignorant certaines caractéristiques fondamentales du risque qui remettent en question sa nature quantifiable et prévisible. Dans cet article, nous allons examiner cette hypothèse à travers trois éléments-clés de l’évaluation éthique des projets de recherches :
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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 | 0.809 | 0.878 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.010 | 0.014 |
| 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; 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".