The systemized exploitation of temporary migrant agricultural workers in Canada: Exacerbation of health vulnerabilities during the COVID-19 pandemic and recommendations for the future
Bibliographic record
Abstract
In 2018, 55,734 jobs in Canadian agriculture were filled by temporary migrant workers, accounting for nearly 20 percent of total employment in this sector. Though referred to as temporary, those migrant workers often fill long-term positions and provide crucial support to the Canadian agricultural industry, which has seen an increasing disengagement from the domestic workforce in the last fifteen years. Health vulnerabilities faced by temporary migrant workers are already well documented. In addition, there are multiple systemic factors inherent within the structure and implementation of the Temporary Foreign Worker Program that contribute to the perpetuation of health inequities within this population. The COVID-19 pandemic has both exacerbated many of these disparities and further increased the risk of labour rights violations and vulnerability to exploitation for these workers. As Canada's 2020 growing season comes to an end, thousands of temporary migrant agricultural workers are returning to their native countries. With planning for next year's growing season already commencing, this timely analysis aims to examine health vulnerabilities faced by TMAWs during the COVID-19 pandemic. Five key areas are examined: occupational injuries, substandard living conditions, psychological difficulties, lack of access to healthcare and barriers in exercising labour rights. Building on this analysis, recommendations for policy and practice aimed at improving migrant workers' health are discussed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".