Falling through the Legal Cracks: The Pitfalls of Using Workers Compensation Data as Indicators of Work-Related Injuries and Illnesses
Bibliographic record
Abstract
Workers compensation statistics are often used to define prevention priorities in occupational safety and health. In this paper we address this issue from a legal perspective. We examine legal and social factors that can make the costs of work-related illnesses and injuries appear less dramatic than they really are, or even disappear altogether. These factors include limited coverage of some sectors of the labour market and certain types of employment injury, low initial acceptance rates of claims for certain kinds of injury and illness, a failure to claim by certain categories of worker and a failure to adequately compensate certain categories of claimant. We illustrate how these factors have a particularly negative effect on women workers as well as on precariously employed workers. The conclusions outline the relevance of our findings for both researchers and policy-makers.
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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 | 0.186 | 0.550 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".