‘Domestic transnationalism’: legal advocacy for Mexican migrant workers' rights in Canada
Bibliographic record
Abstract
Every year thousands of Mexicans travel to Canada to work in Canadian fields and greenhouses under the Mexico-Canada Seasonal Agricultural Worker Program. While the programme is often praised, it has also been the subject of persistent criticism about its failure to meet certain human rights standards. In this article, we examine the legal strategies civil society advocates of migrant workers have adopted to promote migrant workers' rights in Canada. Specifically, we examine legal struggles undertaken by the United Food and Commercial Workers union to challenge Ontario government legislation that does not permit collective bargaining by farmworkers in the province. We argue that this case demonstrates that despite the fact that many of the workers involved are transnationalized, appeals to international bodies or to international human rights standards have been of limited utility in promoting their rights. Despite frequent arguments about the increased relevance of international human rights and citizenship norms and transnational human rights advocacy, in this case the national and sub-national scales remain predominant. The result, we argue, is a form of ‘domestic transnationalism’, in which domestic political actors engage in advocacy within domestic legal institutions to promote the rights of a transnational mobile labour force.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.044 | 0.018 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".