Locating Labour Law: Conflicting Perspectives and the Case of Occupational Health and Safety
Bibliographic record
Abstract
While the need to locate employment and labour law in its social context is now widely recognized, there is significant disagreement over the character of that social context, how law is located in it, and the way that law both shapes and is shaped by its social location. The importance of these disputes is not just theoretical because their resolution shapes the way labour law is written and implemented. Nowhere is this truer than in one particular area of labour law, occupational health and safety (OHS) regulation. This chapter argues that from its origins in the nineteenth century, OHS regulation has been primarily guided by a consensus view of workers’ and employers’ interests in workplace health and safety. Historically, workers periodically resisted consensus-based regimes of regulation, insisting that the protection of their lives and health requires measures that recognize the salience of conflicts of interest and unequal power relations between workers and employers. From time to time workers succeeded in having the law reformed to address their concerns, but these accomplishments were undermined by the successful re-assertion of consensus perspectives in the implementation of regulatory reforms, resulting in regulatory failures that workers have paid for with their lives and health.
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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 | 0.047 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.042 | 0.126 |
| Scholarly communication | 0.027 | 0.018 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.035 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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".