Bibliographic record
Abstract
I provide a complete characterization of Nash implementable allocations of spending in prevention by judgement-proof injurers. This characterization is used to identify the optimal rule that allows for the maximum total spending in prevention. The optimal rule amounts to apply the negligence rule to the deep-pocket (or the victim), that is the injurer who responds the most to monetary incentives under the strict liability rule, and the strict liability rule to everybody else. Je développe une caractérisation complète des allocations de dépenses en prévention par des justiciables à la responsabilité limitée pouvant être mises en place en équilibre de Nash. Cette caractérisation est employée afin d'identifier la règle optimale permettant un maximum de dépenses en prévention. La règle optimale se résume à appliquer la règle de négligence au plus riche (le «deep-pocket» ou la «victime», selon l'interprétation), soit le justiciable qui répond le mieux aux incitations monétaires sous la règle de négligence, et la règle de responsabilité stricte à tous les autres.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".