Are the Supporters of Populist Parties Loyal Voters? Dissatisfaction and Stable Voting for Populist Parties
Bibliographic record
Abstract
Abstract Scholars of electoral behaviour regularly link political dissatisfaction to two types of behaviour: voting for populist parties and unstable voting behaviour. It is therefore not surprising that the electorates of populist parties are generally assumed to be rather volatile. In this article, we argue that this is not necessarily the case – in particular in a context of increasingly strong and viable populist parties. We make use of data from the Comparative Study of Electoral Systems project to show that voters for populist parties are neither more nor less volatile than voters for mainstream parties. Political dissatisfaction among voters for populist parties even increases the likelihood of stable voting for populist parties. The supply of populist parties further conditions the stability of the populist vote, as voters in systems with established populist parties are more likely to vote stably for populist parties. Finally, we find that in a context of strong and stable populist parties, the effect of political satisfaction on vote switching is somewhat reduced.
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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 | 0.015 |
| 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) | 0.009 | 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".