Replication Data for: (Mis)information, Polarization, and Trust in Elections: Longitudinal Evidence from Canada
Bibliographic record
Abstract
Scholars have suggested that we are experiencing a global crisis in trust in elections, with political polarization and misinformation often cited as reasons for distrust. However, empirical evaluations of perceptions of election integrity over time remain scarce. Using data from the Canadian Election Study from 2008 to 2021 and the Media Ecosystem Observatory from 2021, we show that trust in electoral management bodies, measured in terms of confidence, satisfaction, and fairness, has significantly declined since 2019 among Canadians with a right-wing ideology or who support right-wing parties. We find evidence that rising affective polarization, right-wing and social media consumption, and belief in U.S.-based election fraud narratives are associated with significant trust declines in electoral integrity. The results identify key factors linked to the recent politicization of citizens’ perceptions of election fairness and have important implications for the health of our democracy.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.017 |
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".