Expressions of Mistrust Among Canadian Federal MPs
Bibliographic record
Abstract
A crucial barometer of democratic health is trust in others and in institutions. Intensifying mistrust, moreover, has been identified as offering fertile ground to populist movements. This paper investigates the supply of—and the public demand for—mistrust narratives among Canadian politicians using a database of original tweets posted on X (formerly Twitter) in English by federal MPs between 2021 and 2025. Following a dictionary-based approach, we develop a high-frequency mistrust indicator, which we then apply to examine quantitative trends and rhetorical shifts in articulations of mistrust. Our findings reveal a growth in the supply of mistrust narratives among Canadian MPs, which is most pronounced within the Conservative Party of Canada. We further find that tweets expressing mistrust are in especially high demand among right-wing audiences, particularly since the 2022 Freedom Convoy. We interpret these findings as evidence of the role of mistrust narratives in advancing (particularly right-wing) populist political projects in Canada.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".