Contesting Inequality: The Impact of Immigrant Legal Status and Education on Legal Knowledge and Claims-Making in Low-Wage Labor Markets
Bibliographic record
Abstract
Abstract Low-wage Latina/o workers are subject to an array of workplace abuses. This study focuses on whether educational attainment may moderate inequality in knowledge or claims-making across individuals with different legal statuses. This question is motivated by research which, while highlighting the role of education in promoting civic and political engagement, has not examined the interaction between education and legal status for worker claims-making. We draw from the 2008 Unregulated Work Survey, which is representative of the 1.64 million low-wage workers in Chicago, Los Angeles, and New York, three of the largest immigrant destinations in the United States. Using the Latina/o subsample, we test whether education impacts workers’ procedural knowledge of the claims process, as well as their actual claims-making behavior, across four categories of workers: U.S.-born citizens, naturalized citizens, documented noncitizens, and undocumented noncitizens. Our findings reveal that all noncitizens have lower levels of procedural knowledge about how to file a complaint with the government, compared to citizens, across educational levels. However, when it comes to claims-making, we find that education has significant positive impacts for noncitizen workers, especially the undocumented. Our results suggest that education may improve the workplace agency of even the most marginalized workers.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".