Sexism and Gender Stereotyping in International Guest Worker Programs: An Analysis of Two 2016 Petitions Filed Under the North American Agreement on Labor Cooperation
Bibliographic record
Abstract
In July 2016, migrant farm worker advocates and trade unions filed petitions under NAFTA's labor side agreement alleging sex discrimination in recruitment for the Canadian Seasonal Agricultural Worker Program (SAWP) and the U.S. H-2A and H-2B agricultural and low wage visa programs. Because of sex discrimination in recruitment, less than 4 percent of the workers who participate in the programs are women. These two bold and innovative petitions highlight the bifurcation of global migrant labor markets which exclude women from economic opportunity based on gender stereotyping. Discrimination in recruitment and treatment of women in the global migrant labor market is the norm, not the exception. After discussing and comparing the facts and claims raised in each petition under applicable legal frameworks in Canada, the U.S., Mexico and the North American Agreement on Labor Cooperation (NAALC), this article explores possible outcomes of the petitions given the nuances and political environments in the Canadian and U.S. cases and the current state of relations between the Government of Mexico and its North American neighbors. The article places sexism and gender stereotyping in North American guest worker programs in an international context, discussing other examples of sexism in the global labor market and existing norms in ILO Conventions and CEDAW. In the Canadian case, the article argues that the Governments of Canada and Mexico should renegotiate international agreements that form the SAWP to implement the recommendations of the Mexican Council on the Prevention of Discrimination. In the U.S. case, the article argues that the Government of Mexico should pursue the establishment of an Evaluative Committee of Experts (ECE) under Article 23 of the NAALC if the U.S. does not enact and enforce meaningful reforms to eliminate sex discrimination in the H-2A and H-2B visa programs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".