Bibliographic record
Abstract
Ce document de travail caractérise le contrat optimal dans une économie où un agent informé de l'état de la nature doit rapporter cet état à un principal qui ne peut se commettre de manière crédible dans une stratégie de vérification de l'annonce de l'agent. Puisque le principal ne peut se commettre, il devient optimal pour l'agent de mentir avec une certaine probabilité. En supposant qu'il existe T>1 pertes possibles en cas d'accident, que l'agent ne peut feindre un accident (il est restreint à rapporter la perte en cas d'accident,0501s la présence d'un accident est une information de nature commune), le contrat optimal est tel que les hautes pertes sont sur-indemnisées alors que les faibles pertes sont sous-indemnisées en moyenne. Le niveau de sur-indemnisation des hautes pertes diminue toutefois avec la perte elle-même. Le contrat optimal peut ainsi être représenté comme une simple combinaison d'une franchise, d'un paiement forfaitaire et de co-paiements.
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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 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".