National and regional proportion of immigrants and perceived threat of immigration: A three-level analysis in Western Europe
Bibliographic record
Abstract
Immigration is of growing significance to the demographic makeup of Western Europe. A long-standing and highly disputed question is whether a larger number of immigrants are associated with more negative attitudes toward immigration or whether the reverse is true. Previous studies yielded contradictory results on various levels of analysis (national, regional, local). These inconsistencies may partly be linked to what is known as the ‘modifiable areal unit problem’ in spatial analysis. This article seeks to address this issue by analyzing the relationship between the percentage foreign-born and perceived group threat in 15 Western European countries on the national as well as on three differing regional levels ( N = 70, 207, and 624 regions, respectively), together with survey data from the European Values Study’s fourth wave. I expect threat effects to operate through national communication systems while contact and habituation to immigrants to work on the regional level. Consistent with theoretical expectations, the results show a positive correlation between the national proportion of immigrants and perceived threat, while the coefficients are negative on the regional level. More immigration might thus lead to a more negative evaluation of the presence of immigrants in European countries, but apparently not within the regions where most of the newcomers reside. Two recent examples illustrate this seemingly paradoxical relationship. As a methodological result, effect size and statistical significance vary with the delimitation of the regional units of analysis ( Nomenclature des Unités Territoriales Statistiques (NUTS)-1, -2, or -3). This suggests that research in this field should pay more attention to how and why spatial units are defined.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".