Contesting Inequality: The Impact of Immigrant Legal Status and Education on Legal Knowledge and Claims-Making in Low-Wage Labor Markets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it