La recherche en gestion et les comités d’éthique : l’épreuve de la pratique1
Bibliographic record
Abstract
À partir de leur expérience de plusieurs années au sein du Comité d’éthique de la recherche (cer) d’une grande école de gestion, les auteurs explorent plusieurs effets pervers de l’institutionnalisation de l’Énoncé de politique des trois conseils. Éthique de la recherche avec des êtres humains et du fonctionnement des cer. Ces effets pervers renvoient en particulier à 1) la réduction de l’éthique de la recherche à la question des relations entre chercheurs et personnes étudiées, 2) au renforcement d’une vision partielle des rapports de force centrée sur la protection des personnes étudiées, 3) à l’accentuation du fossé entre recherche « sur le terrain » et recherche à partie de base de données quantitatives, 4) aux difficultés à créer les conditions d’un apprentissage collectif en matière d’éthique de la recherche et 5) à la déresponsabilisation de certains chercheurs.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.142 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.016 | 0.102 |
| Scholarly communication | 0.029 | 0.034 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".