From Highly Skilled to Low Skilled: Revisiting the Deskilling of Migrant Labor
Bibliographic record
Abstract
Traditional immigration countries such as United States, Canada, Australia, and New Zealand give preference to migrants with higher education, skills, and professional training that they can transfer to their countries. However, it is not unusual for migrant professionals, especially those from less developed countries, to experience 'deskilling' or occupational downward mobility. Though admitted as professionals based on the immigration policies of the destination countries, many of them are relegated to lower status and lower paying jobs, owing to the nonrecognition of their foreign credentials and the bias for education acquired in the host country or in academic institutions in developed countries, local experience, cultural know-how, and English proficiency. Their foreign credentials and skills often fail to provide the expected occupational rewards and professional development gains which have been a significant part of their motivation to migrate overseas, especially to more developed countries.Deskilling may be viewed in several ways: as a host country's way of filling up labor scarcities in the secondary market by exploiting cheap enclave labor, as a transitional phase for migrants to adjust to the 'standards' of the host country, or as a form of institutionalized discrimination. This paper reviews the deskilling phenomenon to highlight its deleterious effects on migrants' welfare. Some theoretical explanations of deskilling are also examined. Examples of deskilling experiences of different migrant groups show that it is a complex phenomenon that demonstrates the interplay of race, ethnicity, and gender.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".