How passionate is the electorate? A study of emotion based voting in party-centered politics.
Bibliographic record
Abstract
The last quarter of a century has brought a stream of research that shows that voters’ choices at the polls are strongly influenced by their emotions. The evidence, however, is almost exclusively based on data concerning candidate-centered elections, in particular those for the American presidency. This paper examines the role of emotions in party-centered politics. It utilises data from the Dutch Parliamentary Election Study 2006, which asked voters to rate their feelings of enthusiasm, anxiety (worry), irritation, and pride for three major political parties. These measures help explain party evaluations, even if social identity, ideology, and policy preferences are taken into account. In line with the theory of affective intelligence, voters whose primary emotional response was one of anxiety showed stronger effects of cognitive factors, in particular perceived ideological agreement. Contrary to expectations, negative feelings as indicated by low evaluation scores were not so much the result of irritation or worry, but of the absence of enthusiasm. This suggests that feeling thermometer scales are not bipolar measures of positive and negative affect, but unipolar measures of positive affect.
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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 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".