A Rare Moment of Cross-Partisan Consensus: Elite and Public Response to the COVID-19 Pandemic in Canada
Bibliographic record
Abstract
The COVID-19 pandemic has placed nearly unprecedented pressure on policymakers and citizens alike. Effectively containing the pandemic requires a societal consensus. However, a long line of research in political science has told us that polarization tends to occur on highly salient topics because partisans “follow the leader.” Elite consensus is thus essential to fight the COVID-19 pandemic in Canada. We examine the degree of partisan consensus that exists in Canada at the level of political elites and the mass public. At the level of political elites, we quantitatively and qualitatively analyze MP Twitter behaviour and show a massive increase in attention to COVID-19 and find no evidence of any MPs from any party downplaying the pandemic or spreading misinformation. At the level of the mass public, we find no association between Conservative Party vote share and Google search interest in the coronavirus, while survey data show that individual-level partisan differences are small and disappear when controlling for demographics and left-right ideology. Elite and public response to the COVID-19 pandemic can be characterized as a cross-partisan consensus.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".