Individual attitudes towards migration: A re‐examination of the evidence
Bibliographic record
Abstract
Abstract In the literature about the determinants of attitudes towards immigration, some authors emphasize the role of economic factors, while others argue that attitudes are mostly determined by non‐economic factors. This paper evaluates the relative importance of the two. We estimate a structural model of individual attitudes towards immigration, accounting for unobserved individual factors, and use this model to carry out a decomposition analysis of attitudes in 20 European countries. We find that economic mechanisms are significant determinants of attitudes, but that other (non‐economic) factors play a more decisive role in the relation between individual education levels and attitudes to immigration. Résumé. Attitudes individuelles face à l’immigration : réexamen de l’évidence empirique. Dans la littérature portant sur les facteurs qui déterminent les attitudes face à l’immigration, certains auteurs soulignent le rôle des facteurs économiques tandis que d’autres affirment que ces attitudes sont principalement déterminées par d’autres facteurs. Cet article évalue l’importance relative de ces deux approches. Nous estimons un modèle structurel qui explique les attitudes individuelles face à l’immigration, et qui tient compte des facteurs individuels non observés. Nous utilisons ce modèle pour procéder à une analyse par décomposition de ces attitudes dans 20 pays européens. Nous trouvons que les mécanismes économiques influencent de manière significative les attitudes. Cependant, les facteurs non économiques jouent un rôle plus important dans la relation entre le niveau d’éducation des individus et leurs attitudes face à l’immigration.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".