The systemized exploitation of temporary migrant agricultural workers in Canada: Exacerbation of health vulnerabilities during the COVID-19 pandemic and recommendations for the future
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it