Party or Policy? The Role of Policy Partisanship in Voter Decision-Making
Bibliographic record
Abstract
Which matters more for voters, the political party or the policy positions of electoral candidates? We contribute to this long standing debate by analyzing the relative importance of policy content and party cues in the multi-party Canadian context. Using the Canadian case allows us to disentangle the effects of policy and party on voter decision-making, which are closely intertwined in the more polarized and extensively studied U.S. case. First, we employ a conjoint survey experiment to test whether the effect of an electoral candidate’s policy position on their evaluation by voters depends on the implicit party cues that are embedded in the policy. We find that while Canadians often associate policies with specific parties, they do not seem to use these implicit party cues in their evaluation of the candidates, focusing on policy congruence instead. Second, we test whether explicit party cues reduce the weight of policy information in candidate evaluations and find that they do not. Overall, our findings suggest that party cues are not as useful for voters in multi-party systems with low polarization, and that voters rely on policy information to make decisions in these contexts.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".