“It’s like We’re Still in Slavery”: Stress as Distress and Discourse among Jamaican Farm Workers in Ontario, Canada
Bibliographic record
Abstract
For more than fifty years, Jamaican farm workers have been seasonally employed in Canada under the Seasonal Agricultural Worker Program (SAWP). In Canada, these workers live and work in conditions that make them vulnerable to various health issues, including poor mental health. This ethnographic study investigated Jamaican SAWP workers’ mental health experiences in Southern Ontario. Several common factors that engender psychological distress among Jamaican workers, ranging from mild to extreme suffering, were uncovered and organised into five themes: (1) family, (2) work environments and SAWP relations, (3) living conditions and isolation, (4) racism and social exclusion, and (5) illness and injury. I found that Jamaican workers predominantly use the term ‘stress’ to articulate distress, and they associate experiences of suffering with historic plantation slavery. Analysis of workers’ stress discourses revealed their experiences of psychological distress are structured by the conditions of the SAWP and their social marginalisation in Ontario. This article presents and discusses these findings in the context of SAWP power dynamics and concludes with policy recommendations aimed at improving the mental health of all SAWP workers. In foregrounding the experiences of Jamaican workers, this study addresses the dearth of research on the health and wellbeing of Caribbean SAWP 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.014 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".