Effets stratégiques de la qualité des éléments de preuve dans une procédure accusatoire
Bibliographic record
Abstract
Dans une procédure accusatoire, il incombe aux parties de prouver les faits relatifs à leurs prétentions. La recherche de preuve étant coûteuse, chaque partie n’investit dans cette activité qu’en fonction des bénéfices qu’elle en attend. Nous analysons l’effet d’une amélioration de la qualité des preuves potentielles sur le risque d’erreur judiciaire. Ce risque diminue si la demande de preuve des parties n’est pas trop élastique et si celles-ci ont peu de marge de manœuvre dans le tri des éléments qu’elles décident de communiquer. Cependant, lorsque les parties peuvent trier ces éléments à des fins stratégiques, nous montrons que le scepticisme rationnel du juge, combiné aux effets sur la recherche de preuve, peut alors conduire à plus d’erreurs judiciaires. Classification JEL : D82, K41.
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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 | 0.016 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".