How parties can shape their competence reputations: Issue attention, position and performance
Bibliographic record
Abstract
Abstract Issue ownership is an important determinant of the vote, and it is electorally beneficial for parties to build a strong reputation on their core issues. Even though issue ownership has already been studied extensively in the party literature, we know less about how citizens form ownership perceptions. We contribute to this literature by means of two studies on the connection between party behaviour and perceptions of issue ownership, with an empirical focus on issue competence reputations of parties. In Study 1, we combine party‐level information about issue attention, positions and performance with data on competence perceptions from a wide range of national election studies. Study 2 is a pre‐registered conjoint experiment designed to examine the causal link between party behaviour and perceived competence. Our results point to significant effects for all three hypothesised sources of competence reputations. Moving beyond previous work that has argued that competence reputations are mostly stable over time, after accounting for the variation due to parties' popularity, our results show that they fluctuate in the short term and that parties have some level of control over how they are perceived.
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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 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".