Overcoming origin-based preferences by selecting skilled immigrants? Preferences in immigrants’ national origin and social class in Quebec
Bibliographic record
Abstract
Although most liberal democracies have abandoned preferences for national origins in immigrant selection policies, large segments of the local populations continue to prefer immigrants that they perceive to be of similar cultural, religious, and ethnic backgrounds as them. What remains unknown is whether governments can count on the promotion of successful economic integration to ensure acceptance of the greater ethnocultural and religious diversity of immigrants that now settles in host-countries through what has been identified as building a middle-class nation. Relying on an original survey experiment of 2400 respondents in Quebec, we compare reactions to immigrants of different professional status and two national origins (France and Algeria) to investigate if certain types of economic immigration can reduce origin-based preferences. Our results show that origin-based preferences shared by majority group members can be attenuated, but not eliminated by selection based on social class. That said, expectations that immigrants will contribute to Quebec's economy translate into greater acceptance of immigrants of all national origins and social classes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".