Risk Regulation: Technocratic and Democratic Tools for Regulatory Reform
Bibliographic record
Abstract
This article reviews the empirical evidence on the results of regulation of health and safety risks. It notes dramatic variances in the costs per life saved of various health and safety regulations which implies serious misallocations of social resources. The authors argue that problems of over and under regulation are the result of political and regulatory processes insufficiently disciplined by technocratic tools, especially scientific risk assessment and cost-benefit analysis. On the other hand, both scientific risk assessment and cost-benefit analysis are themselves beset by numerous technical and normative frailties, hence requiring in turn that public participation in the regulatory process discipline the use of these technocratic tools so that scientific and technical analysts do not over-step the legitimate bounds of their disciplines and usurp value judgments more properly made ultimately by citizens in a liberal democracy. Hence, science must discipline politics and politics must discipline science. The article develops a set of institutional proposals for risk regulation designed to assign appropriate roles to technocratic and democratic tools in regulatory reform.
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 | 0.080 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.075 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".