Les jugements de contingence chez les individus dépressifs et non dépressifs : une méta-analyse
Bibliographic record
Abstract
Cette étude constitue une méta-analyse centrée sur les jugements de contingence chez les individus dépressifs et non dépressifs. Elle visait à déterminer si les dépressifs présentent des jugements de contingence plus précis que les non dépressifs et à déterminer la robustesse de cet effet en considérant différents modérateurs. Seize études représentant 1167 participants étaient disponibles. Les jugements de contingence sont significativement plus précis chez les dépressifs. Ce résultat varie selon le degré de contingence, mais pas selon le sexe, la sévérité de la dépression ou les autres caractéristiques expérimentales. Ces résultats sont discutés à la lumière de la théorie de la marge optimale d’illusion.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.020 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".