Immigration regimes and schooling regimes: Which countries promote successful immigrant incorporation?
Bibliographic record
Abstract
While Canada is often described as the most and France as one of the least successful countries in the realm of immigrant incorporation, the question remains unresolved of how to evaluate a country’s policies for dealing with immigration and incorporation relative to that of others. Our strategy is to examine the relationships among (1) countries’ policies and practices with regard to admitting immigrants, (2) their educational policies for incorporating first- and second-generation immigrants, and (3) the educational achievement of immigrants and their children. We compare eight western industrialized countries. We find that immigration regimes, educational regimes, and schooling outcomes are linked distinctively in each country. States that are liberal, or effective, on one dimension may be relatively conservative, or ineffective, on another, and countries vary in their willingness and ability to help disadvantaged people achieve upward mobility through immigration and schooling. We conclude that, by some normative standards, France has a better immigration regime than Canada does. Overall, this study points to new ways to study immigration and new normative standards for judging states’ policies of incorporation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".