Foreigners' Impact on European Societies
Bibliographic record
Abstract
The research examines the extent to which attitudes toward foreigners vary across European countries. Using data from the European Social Survey for 21 countries the analysis reveals that foreigners' impact on society is viewed in most countries in negative rather in positive terms. The negative views are most pronounced with regard to foreigners' impact on crime and least pronounced with regard to foreigners' impact on culture. Multi-level regression analysis demonstrates that the negative views tend to be more pronounced among individuals who are socially and economically vulnerable and among individuals who hold conservative political ideologies. The analysis also reveals that negative attitudes toward foreigners tend to be more pronounced in countries characterized by large proportions of foreigners, where economic conditions are less prosperous, and where support for right-wing political parties is more prevalent. The analysis shows that inflated perception of the size of the foreign population is likely to increase negative views toward foreigners and to mediate the relations between actual size and attitudes toward foreigners' impact on society. The findings are presented and discussed in light of sociological theories on individuals and structural sources of public attitudes toward out-group populations and on the role of perceptions in shaping such attitudes.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".