How Discrimination Against Ethnic and Religious Minorities Contributes to the Underutilization of Immigrants’ Skills
Bibliographic record
Abstract
The underutilization of immigrants’ skills, particularly the skills of ethnic and religious minorities, is of considerable concern to policy makers because of its economic and social costs. Recent research suggests that discrimination may be contributing to this well-documented unemployment and underemployment of skilled minority immigrants. In particular, the ambiguity of immigrants’ foreign-acquired skills and personal characteristics may provide a cover for the expression of bias toward immigrants who are religious and ethnic minorities. Experiments controlling for all other variables show that discrimination may influence both the hiring of minority immigrants and reactions to claims of employment discrimination by minority immigrants. Also, factors that reduce the ambiguity of minority immigrants’ credentials and factors that suppress the expression of bias reduce these effects. These findings point to policy interventions that have the potential to improve the labor-market outcomes of skilled immigrants and contribute to host nations’ economic and social outlooks. Interventions should focus not only on skilled minority immigrants and reducing the ambiguity of their credentials and skills but also on members of the host society and their motivation to control prejudiced reactions to minorities.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".