From Worker Victory to Policy Reform: Injured Migrant Workers Fight for Return to Work Justice in Workers’ Compensation in Ontario, Canada
Bibliographic record
Abstract
This article explores the challenges facing injured migrant farm workers in the workers ’ compensation system in Canada's province of Ontario, with a focus on their fight for return to work justice. Told from the perspective of one of the lawyers who represented the workers, it highlights a recent victory achieved by 4 workers in the Seasonal Agricultural Worker Program in defending their rights to workers’ compensation support. The workers’ compensation tribunal decided that the workers’ compensation board must evaluate these workers ’ ability to return to work, access retraining, and receive compensation based on their labor markets in Jamaica—instead of based on fictional job prospects in Ontario. The tribunal also called out the need to consider systemic anti-Black racism in workers’ compensation law and policy. The article analyzes how this legal victory could reshape workers ’ compensation policy in Ontario for injured migrant farm workers. It also discusses the implications of the win for injured workers in other temporary work programs and precarious employment sectors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.010 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".