Structural Data on Immigration or Immigration Perceptions? What Accounts for the Electoral Success of the Radical Right in Europe?
Bibliographic record
Abstract
Abstract Targeting immigrants as a threat to employment, security and cultural cohesion, the radical right has averaged 10 percent of the vote in elections. What drives this vote? Are voters affected by the numbers of foreign‐born individuals in a geographical region, by negative perceptions about immigrants, or both? In this article, I entertain the possibility that it is not the number of foreigners but citizens’ perceptions about immigrants that explain individuals’ tendencies to vote for the radical right. To test this stipulation, I combine European Social Survey (ESS) data on individual perceptions of immigrants for more than 25,000 individuals with macro‐level data on the actual percentage of foreign‐born citizens across 200 European regions. Using a bivariate and multivariate framework, I highlight that it is only the individual perceptions of immigration indicator, and not the number of foreign‐born citizens, that is positively related to higher support for radical right‐wing parties.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".