Shifting Perceptions: Unpacking Public Support for Immigrant Workers Integration in the Labor Market
Bibliographic record
Abstract
This paper investigates public perceptions and support for policies aimed at integrating immigrant workers into domestic labor markets. Through large-scale surveys involving 6,300 respondents from Canada, Italy, and the United Kingdom, we provide new insights into attitudes toward migrant integration policies and the impact of different information provisions on belief updating. We identify three key factors that shape policy support: pre-existing stereotypes about immigrants, awareness of labor market integration policies for migrants, and, most critically, the perceived economic and social impact of these policies. Our findings reveal that providing information about the economic effects of integrating immigrants in the labor market significantly alters perceptions and increases support for these policies. Notably, explanations of the economic mechanisms underlying these policies are more effective than simply presenting policy effects or real-life stories of integration challenges. The survey also identifies the primary barriers to policy support, with fairness considerations toward unemployed native workers emerging as the top concern. It reveals that addressing individuals’ specific concerns through tailored mitigation measures can enhance support for policies aimed at better integration migrants. Nevertheless, a significant challenge remains in overcoming mistrust in the government’s commitment and ability to effectively implement these policies and accompanying measures.
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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 | 0.007 | 0.023 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".