Conditions of far-right strength in contemporary Western Europe: an application of Kitschelt's theory*
Bibliographic record
Abstract
Abstract Applying the demand-side claims of Kitschelt's theory, and the expectation that electoral systems affect voter choice, this article provides an explanation of cross-national variation in support for new radical right (NRR) parties between 1982 and 1995. After discussing concepts and measures, two versions of qualitative comparative analysis (Boolean analysis and fuzzy-set analysis) are applied to data for ten West European countries. The results suggest that, in combination with electoral systems that had larger district magnitudes, NRR strength resulted from a restructuring of the space of party competition due to post-industrialism and growth in the welfare state. Convergence between major parties of the left and right was not among the combination of conditions that led to NRR success. Apart from demonstrating that fuzzy-set analysis can yield a simpler explanation than Boolean analysis, this study reveals anomalous NRR outcomes for Austria, Belgium and France.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| 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".