Bibliographic record
Abstract
Ce papier rapporte les résultats d'une expérience qui cherchait à tester si les individus percevaient correctement les risques mortels auxquels ils font face personnellement. Les résultats suggèrent que la perception des sujets est autant biaisée lorsqu'ils font des prédictions pour leur propre groupe d'âge ou pour la population entière (c'est-à-dire que les individus surestiment les risques rares, et sous-estiment les risques les plus communs). Cependant, l'hypothèse que les individus possèdent une meilleure connaissance de leurs propres risques ne peut être écartée complètement puisque les réponses du sondage sur leur propre groupe d'âge sont plus homogènes et mieux ordonnées. Finalement, nous démontrons que l'administration des sondages en série a pu générer des effets d'ancrage (anchoring effects) qui pourraient expliquer le fait que nos conclusions diffèrent sensiblement d'une étude précédente.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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, 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".