Partisan agenda misperceptions in Canada
Bibliographic record
Abstract
Do citizens accurately perceive partisan political priorities? Democratic politics often unfolds as a strategic game, where both partisans and voters align their preferences and make choices to shape the political landscape and achieve their goals. For this strategic interaction to succeed, players need a clear understanding of their opponents' tactics and priorities. However, several studies show that partisans frequently misperceive both their in-group and out-group on several dimensions, including ideology, policy positions, and group composition. In a previous study, we examined partisan agenda perceptions in Canada. By partisan agenda, we mean the importance each major partisan group assigns to specific policy areas (e.g., the economy, environment, crime, ethics in politics). At a descriptive level, we found that partisans systematically underestimate other groups' priorities and overestimate their own. Certain issues are also more prone to misperception than others. In our multivariate analyses, we found that moderate media exposure and moderate affective polarization improve accuracy, while inattentive individuals, political junkies, and those with high affective polarization tend to be the least accurate. Generally, partisans underestimate the level of attention out-partisans give to various issues, although their underestimation is less pronounced than that of non-partisans. In the present study, we continue to explore partisan agenda perceptions and their correlates, with improved measurements of partisan priorities. All participants will first be asked to report their own issue priorities, ranking political issues by importance in four categories (high, low, unsure). Later in the survey, they will be asked the same question regarding in- and out-party partisans.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".