Bibliographic record
Abstract
Chen et al 1 illuminate the increasing risk of workplace violence to women in the education sector through analysis of lost time claims and emergency department visits in Ontario, Canada. The authors compare workplace violence lost-time injury workers’ compensation claims as reported to the Workplace Safety & Insurance Board from 2002 to 2015 across industries, specifically focusing on healthcare that traditionally has high rates and education that has been understudied. Unlike education, workplace violence in the healthcare sector has received much attention. For example, in the USA, the National Institute for Occupational Safety and Health created resources for nurses,2 home health aides3 and workplace violence training and policy considerations.4 Chen et al 1 found that while the rate of violence to women in healthcare declined, the education sector unfortunately went in the opposite direction. This is a sounding of the alarm that another sector of human service work—and one predominated by women—is experiencing risk. Perhaps the education sector can benefit from interventions similar to those healthcare. It is …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".