Bibliographic record
Abstract
Part 1 Optimality overall: the economic theory of public enforcement, Polinsky and Shavell the economics of enforcing air pollution controls, Downing and Watson firm behaviour under imperfectly enforceable pollution standards and taxes, Harford enforcement costs and the choice of policy instruments for controlling pollution, Malik. Part 2 Elaboration of themes - the design of penalties: the structure of penalties in environmental enforcement - an economic analysis, Segerson and Tietenberg guilty until proven innocent - regulation with costly and limited enforcement, Swierzbinski garbage recycling, and illicit burning or dumping, Fullerton and Kinnaman. Part 3 Self-reporting discharges: self-reporting of pollution and the firm's behaviour under imperfectly enforceable regulations, Harford self-reporting and the design of policies for regulating stochastic pollution, Malik. Part 4 Extensions of the basic template - using ex-post liability: firm behaviour and regulatory control of stochastic environmental wastes by probabilistic constraints, Beavis and Dobbs uncertainty and incentives for nonpoint pollution control, Segerson. Part 5 Using the regulatory record: cooperation, deterrence, and the ecology of regulatory enforcement, Scholz game models for structuring monitoring and enforcement systems, Russell an integrated strategy to reduce monitoring and enforcement costs, Hentschel and Randall. Part 6 Private and voluntary approaches: optimal standards with incomplete enforcement, Vicusi and Zeckhauser public mechanisms to support compliance to an environmental norm, Stranlund private enforcement of federal environmental law, Naysnerski and Tietenberg. Part 7 Empirical work - describing the M&E situation in the US: monitoring and enforcement, Russell environmental crime and punishment - legal/economic theory and empirical evidence on enforcement of federal environmental statutes. Part 8 Do monitoring and enforcement efforts make a difference?: effectiveness of the EPA's regulatory enforcement - the case of industrial effluent standards, Magat and Viscusi environmental inspections and emissions of the pulp and paper industry in Quebec the costs and benefits of oil spill prevention and enforcement, Cohen. Part 9 Cost and benefits of monitoring and enforcement: standard setting with incomplete enforcement revisited, Jones. Part 10 Explaining the behaviour of enforcement agencies: the revealed preferences of state EPAs - stringency, enforcement and substitution.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".