Immigrant and Migrant Workers Organizing in Canada and the United States
Bibliographic record
Abstract
Across Canada and the United States, immigrant workers face important obstacles at work and in the broader society, whether their immigration status is temporary, permanent, or nonexistent. Hyper-precarious workers of all status groups, and their allies in unions and worker centers, are organizing to improve their conditions. In this book, Jorge Frozzini and Alexandra Law, two longtime volunteers with a Canadian worker center, draw on their own experience, in-depth interviews, and academic work from the fields of law, communication studies, and social movement theory, to produce a tactically focused, theoretically informed introduction to immigrant worker organizing in a neoliberal era. Frozzini and Law describe the phenomenon of employment precarity in the context of U.S. and Canadian labor history, explaining how union certification and collective bargaining function under the law. Without directing activists toward any single best strategy, they cover tactical and ethical questions raised when organizers offer casework as a recruitment and research tool. The royalties from this book will go to the Immigrant Workers Centre, Montreal.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".