Who Makes the Rules? Establishing Occupational Health and Safety Regulations
Bibliographic record
Abstract
Appropriate regulations are fundamental to effective protection for occupational health and safety. This paper examines the process by which regulations are written and adopted. Ideally, a body of reliable scientific evidence would point to the need for regulation. A body of health and safety experts can examine the data and prepare regulatory language for implementation by the appropriate body. Normally, a consultative body of experts from management, government, labour and academia oversees this process. Ultimately, senior decision makers determine the regulation to be adopted. Experience with ergonomic regulations in the US and Canada show that this linear process can be interrupted at many points. In the US, no national ergonomic regulations exist after decades of effort offset by political intervention, while in British Columbia the adoption took years to achieve. The parties’ health and safety experts are crucial. They must understand the problems of regulation and enjoy the confidence of senior officials in their organizations to offset political opposition.
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 | 0.144 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.024 | 0.020 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.015 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".