Challenges to established parties: The effects of party system features on the electoral fortunes of anti‐political‐establishment parties
Bibliographic record
Abstract
Abstract The rise of parties that challenge the political establishment has recently sparked the interest of political scientists. Scholars have identified several factors that lie behind the success of such anti‐political‐establishment parties. Most empirical studies, however, have concentrated their attention either on the importance of electoral system features or on the effects of socioeconomic conditions. This article focuses instead on the role that party system factors play in the electoral success of these parties. Using three data sets from studies conducted in three different time periods it tests two seemingly contradictory hypotheses. On the one hand, the claim that where the established parties have converged toward centrist positions and thus fail to present voters with an identity that is noticeably different from their established competitors, the electorate will be more susceptible to the markedly different policies put forward by anti‐political‐establishment parties. On the other hand, there is the argument that these parties profit more from increasing polarization and the subsequent enlargement of the political space than from a convergence toward the median. The results of the analyses show that anti‐political‐establishment parties generally profit from a close positioning of the establishment parties on the left‐right scale. However, there is no consistent support for the notion that party system polarization by itself is associated with an increase in the support for parties that challenge the political establishment.
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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 | 0.002 | 0.018 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".