Labour struggles for workplace justice: Migrant and immigrant worker organizing in Canada
Bibliographic record
Abstract
This article explores the dynamics of labour organizing among migrant and immigrant workers in Canada, focusing on two case studies: first, recent efforts to organize migrant farmworkers in the Seasonal Agricultural Workers’ Program; and, second, the work of the Immigrant Workers’ Centre in Montreal. The Seasonal Agricultural Workers’ Program, which employs workers from Mexico and Caribbean countries, is often viewed by policymakers and employers as an example of ‘best practices’ in migration policy. Yet workers in the program experience seasonal employment characterized by long hours and low wages, and are exempt from many basic labour standards. The Immigrant Workers’ Centre formed in 2000 to provide a safe place for migrant and racialized immigrant workers to come together around problems in their workplaces. Through these case studies, we examine labour organization efforts including advocacy and grassroots organizing through the Immigrant Workers’ Centre and legal challenges attempting to secure recognition of freedom of association rights for farmworkers. The article explores the ‘limits and possibilities’ of these strategies, and concludes by assessing the implications for labour organizing among the growing numbers of migrant and immigrant workers employed in a wide range of low-wage, low-security occupations due to the recent expansion of Canada’s Temporary Foreign Worker Program.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.072 | 0.015 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".