Compensating Work-Related Disability: Theory, Politics and History of the Commodification-Decommodification Dialectic
Bibliographic record
Abstract
In 2015, the last year for which we have complete Canadian data, workers' compensation boards recognized that 852 Canadian workers died from work-related injuries and diseases and 232,629 workers experienced disabling injuries requiring them to take time off work. About 13 percent of those injured will have permanent disabilities of varying severity.These figures significantly underestimate the true burden of work-related disability for at least three reasons. First, the percentage of the paid Canadian workforce covered by workers' compensation has been shrinking. In 2008, it was estimated to stand at about 80 percent, although coverage bounced back to about 85 percent in 2015.Second, there is widespread evidence of claims suppression and underreporting of lost-time injuries. A 2014 review estimated that workers do not claim 20 percent of their injuries and illnesses and that employers do not report 7-8 percent of injuries, misreport 3-9 percent of lost-time injuries as non-lost-time injuries, and actively suppress some inestimable number of eligible claims.Finally, for a claim to be recorded it must be accepted by the compensation board, and there is evidence that compensation boards are rejecting claims more frequently. For example, in Ontario, the percentage of denied claims increased from 4 percent in 2001 to 8 percent in 2010.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.082 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".