Migrant Agricultural Workers’ Experiences of Support in Three Migrant‐Intensive Communities in Canada
Bibliographic record
Abstract
Canada has intensified its reliance on temporary foreign workers, including migrant agricultural workers (MAWs) who have contributed to its agriculture sector, rural economies, and food security for decades. These workers live and work in rural communities across Canada for up to two years. Thousands of MAWs engage in recurring cyclical migration, often returning to the same rural communities in Canada for decades, while others are undocumented. Yet MAWs do not have access to the supports and services provided for immigrant newcomers and pathways for permanent residence. The exclusion of these workers from such entitlements, including labour mobility, reinforces their precarity, inhibits their sense of belonging, and reflects the stratification built into Canada’s migration regime. This article draws on interviews with 98 MAWs in three migrant‐intensive regions in southwestern Ontario to examine how workers construct and describe support in relation to co‐workers, employers, residents, and community organizations. Drawing on conceptualizations of support as an important vehicle for social connection and inclusion that comprises social and citizenship belonging, we document how the strategies MAWs employ to forge connections are enabled or undermined by Canada’s Temporary Foreign Worker Program, community dynamics, and the broader forces of racialization, gender, and exclusion. This article contributes to the limited scholarship on the support landscape for MAWs, whose experiences foreground the contested nature of belonging and inclusion among migrant populations across smaller cities and rural areas.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.039 | 0.010 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".