Personal issue importance effects on voters’ perceptual accuracy of party issue positions
Bibliographic record
Abstract
The accuracy of voters’ party position perceptions is critical for the functioning of representative democracy, and recent comparative work has shown that voters generally have accurate perceptions of parties’ left-right ideological positions. Yet, we know little about how the left-right results generalize to single issues and how personal issue importance evaluations shape accuracy. Building on existing work on the consequences of issue importance, we argue that voters seek more information and become more accurate in their perceptions of party issue positions on those issues they deem personally important. However, we also posit that this is likely a curvilinear relationship. For highly important issues, voters are more likely to have strong priors and engage in projection effects of assimilation and contrast, limiting their ability to accurately perceive party positions on highly important issues. Using original voter – and expert-level surveys from ten advanced democracies, we show support for our expectations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".