Le biais de confirmation en recherche
Bibliographic record
Abstract
Cet article traite du biais de confirmation dans le domaine de la recherche. Dans la première partie, nous présentons le critère de réfutabilité comme le meilleur antidote contre ce problème. Par la suite, nous abordons quatre aspects de la culture scientifique susceptibles d’alimenter le biais de confirmation en recherche : l’impératif de publication (Publish or Perish), la valorisation des résultats positifs dans les revues savantes, la rareté des études de reproductibilité, une condition pourtant essentielle de la méthode scientifique, certains aspects de la recherche qualitative. Dans la dernière partie, nous illustrons la nature du biais de confirmation en recherche à l’aide de quatre exemples.
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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 | 0.755 | 0.892 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.011 | 0.058 |
| Scholarly communication | 0.028 | 0.036 |
| Open science | 0.011 | 0.022 |
| Research integrity | 0.020 | 0.027 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".