Bibliographic record
Abstract
Abstract In many established democracies, vote choices are growing more volatile over time. This book assesses how changes in voters’ decision making process have contributed to this change. The first part of the book examines the evidence for the claim that the increase in volatility results from a shift in weight from long-term to more short-term determinants of the vote choice. This overview and the analyses that are presented highlight the limitations of existing theories of electoral change and call for novel explanations for voter volatility. The second part of the book makes the argument that group-based cross-pressures are an important source of volatility. Such cross-pressures, that results from the fact that citizens’ socio-demographic characteristics and group-memberships pull them in different partisan directions, imply voters’ decision making process lacks constraint, ultimately making the vote choice more volatile. The book tests this argument by means of longitudinal election survey data from eight established democracies, which allows tracing changes in the vote choice process since the 1950s. The over-time increase in levels of group-based cross-pressures provide a first indication of their importance for explaining over-time changes in voting behaviour. The empirical analyses that are presented next provide more evidence that is in line with this theoretical argument. The results show that group-based cross-pressured voters are less likely to be partisan, are less guided by short-term determinants when choosing a party, make their vote choice later and switch parties more. Analyses that make use of panel survey data confirm these key findings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".