More of the same? Migrant agricultural workers’ health, safety, and legal rights in the COVID-19 context
Bibliographic record
Abstract
In this paper, we report on research findings from a cross-sectional survey with 143 primarily Mexican migrant agricultural worker respondents in British Columbia (BC), Canada. Participants reported high rates of experiences of threats and violence by employers, limited faith in the follow-through of both Canadian and country-of-origin authorities when reporting concerns, and a unanimous lack of knowledge in how to file a claim of a legal matter (e.g., housing, human rights violation). Most participants also reported that they believed they would receive poorer health care in relation to their Canadian counterparts and that their privacy would not be protected. While certain indicators, such as knowledge of resources for transportation, translation, and legal advocacy were higher than previous research would suggest, most participants did not feel confident that more serious issues would be addressed if they sought help. Our results suggest migrant workers in BC report similar, or even higher, rates of experiences and expectations of poor social support, legal protection, and health care in comparison to prior research in this region and elsewhere. While further research would be required to confirm this hypothesis, the impact of COVID-19 on this population is undeniable. Our findings highlight the need for greater regional and provincial commitments to fund targeted services for migrant agricultural workers that address the unique barriers they face. Additionally, greater attention and funding must be dedicated to supporting this population to navigate and access services that already exist. Together, dedicated initiatives could make a major difference for this workforce. Federal investments in support services of this nature would ensure the sustainability of such efforts. In addition, reforms to temporary migrant agricultural programs, such as open work permits and immediate access to permanent residence, would better afford workers opportunities to access the rights and protections that are currently out of reach for many.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".