Individual Predictors of the Radical Right-Wing Vote in Europe: A Meta-Analysis of Articles in Peer-Reviewed Journals (1995–2016)
Bibliographic record
Abstract
In this article, we summarize the individual demand-level factors explaining the radical right-wing vote in European countries. To do so, we first review 46 quantitative peer-reviewed articles featuring the individual vote choice in favour of a radical right-wing party as the dependent variable. To identify relevant articles, we use Kai Arzheimer’s bibliography on the radical right and employ the following inclusion criterion: the articles must be written in English, they must use the individual vote for a radical right-wing party as the dependent variable, they must use a quantitative methodology and they must include some type of regression analysis. Using this strategy, we conduct a meta-analysis of 329 relevant models and find that over 20 individual variables are tested. Because many variables such as attitudes towards immigration, employment, age, education and gender only show moderate success rates in attempting to explain an individual’s propensity to vote for the radical right, we complement the review of quantitative studies with an analysis of 14 qualitative publications. The review of these qualitative works shows that the processes through which somebody becomes a voter, supporter or activist of the radical right are often more complex than the commonly used surveys can portray them. Frequently, feelings of relative economic deprivation and dissatisfaction with the political regime trigger an awakening that makes individuals seek engagement. However, the processes behind this awakening are complex and can only be partially captured by quantitative studies.
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".