Bibliographic record
Abstract
En raison de l’utilisation croissante de l’analyse scientifique dans les enquêtes criminelles, nous assistons à un retour en force du calcul des probabilités, longtemps banni des tribunaux. Toutefois, la mauvaise compréhension de celui-ci par le jury limite son utilité, ce qui a conduit au cours de la dernière décennie à un effort de pédagogie visant à améliorer la présentation des arguments mathématiques aux personnes peu familières des méthodes de raisonnement numérico-logique. Ceci a mis en lumière un problème longtemps ignoré : pour des raisons psychologiques, nous n’analysons pas de la même manière les évènements aléatoires (accidents ou catastrophes naturelles) et ceux résultant d’actes commis par des êtres humains (meurtres, vols…). Ainsi, nous sommes amenés à surestimer la probabilité des deuxièmes par rapport aux premiers, même lorsque les probabilités réelles sont identiques.
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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".