Perceived Democraticness of Parties From Citizens’ Perspectives: Evidence From Canada
Bibliographic record
Abstract
© The Author(s) 2025. Experts consider some parties to be democratic and others undemocratic, based on criteria related to political pluralism. But we know little about how citizens perceive a party’s commitment to democratic norms and whether these perceptions are vulnerable to change. Building upon the motivated reasoning literature, we answer these questions with data from a nationally representative sample in Canada – a well-regarded liberal democracy. First, our descriptive findings show that voters uniformly engage in motivated responding, perceiving their in-party as more committed to democratic norms than the out-party(-ies). Second, leveraging the 2022 Canadian trucker convoy, we prime respondents about the mainstream parties’ undemocratic behaviours (according to scholarly standards). Our experiment demonstrates asymmetrical information updating that supports motivated reasoning: voters maintain their in-party perceptions but they update perceptions of out-parties to be more undemocratic if they hold strong opinions about the convoy. We discuss how these findings enrich the democratic recession literature.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".