Introduction to a Special Issue on the Impact of Immigrant Legalization Initiatives: International Perspectives on Immigration and the World of Work
Bibliographic record
Abstract
This article is the third in a series to celebrate the 70th anniversary of the ILR Review. The series features articles that analyze the state of research and future directions for important themes the journal has featured over its many years of publication. In this issue, we also feature a special cluster of articles and book reviews on one of the most critical labor market issues across the globe—the legalization and integration of immigrants into national labor markets. Despite the urgent need for immigration reform in the United States, there is a paucity of US research that looks at the impact of a shift from unauthorized to legal immigrant status in the workplace. The US immigration literature has also paid little attention to immigrant legalization policies outside of the United States, despite the fact that other countries have implemented such policies with far more regularity. The articles in this special issue draw on studies of legalization initiatives in major immigrant destinations: Canada, Italy, and the United Kingdom. Together they underscore the importance of cross-national perspectives for understanding the range of legalization programs and their impact on immigrant workers, the workplace, and the labor market. These findings contribute to key questions in migration scholarship and inform the global policy debate surrounding the integration and well-being of immigrants.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".