‘Illegalized’ Migrant Workers and the Struggle for a Living Wage
Bibliographic record
Abstract
A higher proportion of workers are earning sub-poverty wages today, compared to few decades ago. Illegalized migrant workers have been disproportionately affected by this trend through super-exploitative employer practices. To improve the wages of low-wage workers, members of unions, community groups, activists, and support coalitions have launched living wage campaigns in cities in the USA, UK and, more recently, Canada. Recognizing that illegalized migrant workers’ lack of legal status is valuable to neoliberalism’s economic “success”, yet at the same time, subjects them to arrest and/or deportation by federal immigration authorities, this paper examines modern living wage campaigns, and how they have incorporated the situation of illegalized migrant workers into their agenda. A review of the literature shows that living wage campaigns have not been very successful in achieving their broad goals while at the same time protecting low-waged illegalized migrant workers. These findings indicate that current and future living wage campaigns should consider working closely with Sanctuary City campaigns to improve their strategies for protecting illegalized migrants from arrest and/or deportation while working to improve the working and living conditions of low-waged workers, including the illegalized.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".