Public issues or issue publics? The distribution of genuine political attitudes
Bibliographic record
Abstract
Abstract There is an inherent conflict between the political marketing model of humans and pioneering theories in electoral behavior research. While political marketing logic implies an issue-based and highly volatile voting behavior, voting theories conventionally assume that positional issues have little effect on how individuals vote, and so parties have little incentive to develop issue-based electoral strategies. However, few people would challenge the role that marketing now plays in the modern campaign process. How can we reconcile these theories? This paper revisits the role and impact of positional issues on voting behavior by testing whether specific issues affect different subgroups of voters as contended by the ‘issue-public’ theory. The results show that previous models underestimate issue voting. Once measurement accuracy is improved and the salience-based heterogeneity of issue effects is taken into consideration, positional issues have non-negligible effects on individual vote choice. Furthermore, salience-based heterogeneity is shown to explain better the variation in issue voting than heterogeneity based on political sophistication.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".