Shifting parties, rational switchers: Are voters responding to ideological shifts by political parties?
Bibliographic record
Abstract
According to spatial theories of voting, voters choose parties that are ideologically close to themselves. A rich literature confirms the presence of a close connection between the positions of voters and parties, but findings from cross-sectional analyses of spatial voting might be driven by endogeneity biases. We argue that for investigating the impact of ideological distance on the vote, spatial theories of voting should be tested dynamically. Taking a Downsian perspective on voting behaviour, we assume that changes in parties’ ideological positions should cause voters to switch parties from one election to another. In doing so, we also contribute to work on responsiveness to political parties. For testing the role of spatial voting dynamically, we make use of election panel surveys in four established democracies: Germany, the Netherlands, New Zealand and Sweden. The results presented in this article suggest that parties’ ideological shifts may indeed cause voters to switch parties, in particular when the party closest to them changes positions, but that the overall impact remains limited.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".