COVID-19 Pandemic and Structural Barriers for Migrant Agricultural Workers in Ontario
Bibliographic record
Abstract
COVID-19 has exposed and exacerbated many longstanding barriers and shortcomings in labour protections for migrant workers in Canada. This paper focuses on the situation of workers under the Seasonal Agricultural Workers Program (SAWP) in Ontario, demonstrating how the COVID-19 pandemic has exposed and greatly aggravated the already precarious conditions of migrant workers. It explores the employment, labour and immigration law frameworks that render SAWP workers particularly vulnerable to exploitation and harm, both during pandemic and non-pandemic times. While some government policy and legislative responses have sought to respond to the increased vulnerability of migrant agricultural workers to the virus, fundamental changes in both the immigration and labour spheres are necessary to fix the structural causes of migrant agricultural workers’ vulnerability. This paper suggest that the pandemic has created not only an unprecedented urgency for systemic change, but also an unprecedented opportunity. Given the current broad shifts in public ideas about employment, health, and vulnerability, as well as mainstream public attention to the plight of migrant farm workers, I suggest that there is now an unprecedented space in Canadian public policy discourse to advance the urgently needed structural changes to protect the rights of migrant farm workers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".