Setting standards for credible compliance and law enforcement
Bibliographic record
Abstract
In this paper we examine the setting of optimal legal standards to simultaneously induce parties to invest in care and to motivate law enforcers to detect violators of the law. The strategic interaction between care providers and law enforcers determines the degree of efficiency achieved by the standards. Our principal finding is that some divergence between the marginal benefits and marginal costs of providing care is required to control enforcement costs. Further, the setting of standards may effectively substitute for the setting of fines when penalties for violation are fixed. In particular, maximal fines may be welfare reducing when standards are set optimally. Nous considérons dans cet article la détermination, en information incomplète, de normes légales optimales pour à la fois inciter les citoyens à faire preuve de diligence (prévention) et motiver les agents de la paix à veiller au respect des lois. L'interaction stratégique entre citoyens et agents de la paix détermine l'efficacité des normes choisies. Notre résultat principal est à l'effet qu'un écart entre bénéfices marginaux et coûts marginaux de la diligence est nécessaire afin de réduire les coûts d'application des lois. De plus, les normes peuvent être un substitut aux amendes lorsque les pénalités pour infraction sont fixes. Des amendes maximales peuvent en particulier être contre‐indiquées lorsque les normes sont optimalement déterminées.
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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 | 0.026 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".