Band-Aid on a Bullet Wound—Canada’s Open Work Permit for Vulnerable Workers Policy
Bibliographic record
Abstract
In June 2019, the Government of Canada implemented the Open work permit for vulnerable workers (OWP-V) policy, authorizing immigration officers to issue open work permits to migrant workers on employer-specific work permits if they demonstrate reasonable grounds to believe that they are experiencing abuse or are at risk of abuse in their workplace. Drawing on research conducted by a community organization on the impact of the policy, this article examines the policy’s potential to remedy the problematic effects of the employer-specific work permit and whether it has been implemented efficiently. Semi-structured interviews were conducted with organizations that provide direct legal and social support to migrant workers in Canada. Additionally, two datasets regarding the role of the OWP-V policy in IRCC’s employer compliance regime were analyzed. The research concludes that the OWP-V policy cannot be expected to counteract the high risk of abuse imposed on workers through the employer-specific work permit. Numerous barriers were identified that make it difficult for migrant workers to apply for the permit. The small number of OWP-V permits issued in proportion to the number of employers authorized to hire migrant workers makes it unlikely that the policy will significantly impact employers’ propensity to comply with the program conditions.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".