Justice on Our Fields: Can 'Alt-Labor' Organizations Improve Migrant Farm Workers' Conditions?
Bibliographic record
Abstract
This article examines how non-traditional labor organizations, also known as “alt labor,” can improve the working conditions of migrant farm workers in the United States and Canada. I consider the work of three labor organizations — the Agricultural Workers Alliance (Canada) (“AWA”), Justice in Motion (U.S.) (“JIM”), and the Coalition of Immokalee Workers (U.S.) (“CIW”) — by focusing on the variety of “legal engagements” that these organizations have to create better working conditions for migrant farm workers. I argue that labor organizations engage with the law in numerous ways, including: improving the rights consciousness of workers; supplementing the work of regulators to increase compliance; undertaking private enforcement of their own; instituting new rights and entitlements through court challenges; building and coalescing social movements; and designing and implementing private regulatory system. I find that the AWA and JIM perform important work to build the rights consciousness of workers and improve compliance with existing legal standards in ways which public regulators are unable to do. However, most workers do not bring forth claims because they fear employer retaliation. The CIW, on the other hand, has devised a private regulatory system that overcomes some of the limitations of public regulatory systems, for example, by allowing farm workers to vindicate their rights regardless of their migration status. Most importantly, the CIW’s private regulatory system requires business entities at the top of the supply chain to take responsibility for working conditions on farms. This system engages with the political economy of the food system because those businesses at the top of the supply chain are best positioned to effect working conditions. I conclude by suggesting that labor organizations actively trying to achieve justice on our fields, like the AWA, JIM and CIW, may point the way for a rejuvenated labor movement.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".