Usual suspects? Public views about immigrants’ impact on crime in European countries
Bibliographic record
Abstract
Using data from the 2002/3 module of the European Social Survey project, this study examines the relationship between public views about immigrants’ impact on crime and measures of criminal behavior in 21 countries of Europe. The results from hierarchical regression models show that perceptions about immigrants’ impact are unaffected by personal experience with crime and by contextual measures such as the homicide rate, prison population rate, and ratio of foreign inmate to non-European foreign population. The analysis further reveals that perceived immigrants’ impact on crime is sensitive to having friends among immigrants, residing in an ethnic neighborhood, having affinity with right-wing ideologies, as well as several socio-demographic characteristics. At the country level, perceptions that immigrants worsen crime problems are more evident in societies harboring larger stocks of non-European immigrants, but such views are not affected by economic circumstances. These findings imply that Europeans’ expressions of concern regarding immigrants’ impact on crime may be a guised form of prejudice against foreigners, as they seem to be nurtured less by fear of crime and more by fear of immigrants. The reported results are discussed with respect to the restrictiveness of immigration regimes and the practice of criminalizing foreigners.
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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 | 0.004 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".