The Trump Effect? Right-Wing Populism and Distrust in Voting by Mail in Canada
Bibliographic record
Abstract
Do Donald Trump's attacks on voting by mail influence how some Canadians view mail-in ballots? The Trump effect on views and behaviors surrounding voting by mail has been well documented in the United States. North of the border, more Canadians than ever voted by mail in the last general election. In this study, we consider how right-wing populism is associated with trust in voting by mail among Canadians. Specifically, we seek to test two main hypotheses. First, we consider whether Canadians holding populist views-and, in particular, those holding right-wing populist views (would-be Trump supporters)-are less trusting of voting by mail. Second, we consider whether political media exposure amplifies this association. We analyze data from both the 2021 Canadian Election Study and Democracy Checkup Survey. We find that those who hold populist views clearly have less trust in voting by mail. This is especially true among right-leaning individuals. Furthermore, as in the United States, this effect is moderated by one's level of political media exposure, with higher levels of political media exposure amplifying the effect of populist views on trust in voting by mail. Our findings, therefore, suggest that the politicization of mail-in voting by President Trump has important implications for the legitimacy of the electoral system not only in the United States, but also in Canada and potentially in other parts of the world.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".