Issue salience, issue ownership, and issue-based vote choice." Electoral Studies
Bibliographic record
Abstract
According to the issue ownership theory of voting, voters identify the political party that they feel is the most competent, or the most credible, proponent of a particular issue and cast their ballots for that issue owner. Yet, the actual micro-level mechanism of such behavior has seldom been examined in the literature. We assess this question and, in the process, offer a refinement to the original model of issue ownership. We argue that while party ownership of an issue is important to individual vote choice, its effect is mediated by the perceived salience of the issue in question; issue ownership should only affect the voting decision of those individuals who think that the issue is important. The conditional effect of issue salience on ownership-based voting is demonstrated through individual-level analyses of vote choice in the 1997 and 2000 Canadian federal elections. The results strongly suggest that salience should be more explicitly integrated into the formulation of the theory and its empirical testing. The observed decline in the explanatory power of sociological determinants of vote choice over the past few decades has prompted scholars to more closely consider the role of political issues in individual electoral decisions. One explanation of issue-based vote choice
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".