1 Workplace Injuries and ESA Violations among Young Workers:
Bibliographic record
Abstract
Over the last decade, governments across Canada have responded to the specific problem of workplace injuries among young workers through an increased emphasis on consciousness raising media programs, education and training. This shift in attention was motivated by research evidence on the higher rates of injuries among young workers (Breslin and Smith 2005; Breslin et al, 2007; Wegman and Davis, 1999; Salminen, 2004), the higher costs associated with compensating disabled young workers and the efforts of unions and other activists (often the parents of children killed on the job) to bring more attention to the issue. While compensation statistics in Ontario and elsewhere suggest a general decline in injury rates, including a decline among young workers (WSIB, 2007), the evidence also indicates that the drop has been slower among young workers and that they continue to have higher rates of injuries than older workers (Breslin et al., 2006b). Although some of the existing literature indicates that young workers are more reluctant to report injuries and unsafe working conditions, relatively little research has been conducted on the reasons for under-reporting. Interestingly enough, while anecdotal evidence suggests that young workers are also subject to higher rates of Employment Standards violations, there is very little research to date on age related ESA violations in Ontario and on whether there is under-reporting here as well.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".