Beliefs about COVID-19 in Canada, the U.K., and the U.S.A.
Bibliographic record
Abstract
The COVID-19 pandemic presents an unprecedented challenge to humanity. Yet there seems to be substantial variation across individuals in knowledge and concern about COVID-19, as well as in the willingness to change behaviors in the face of the pandemic. Here, we investigated the roles of political ideology and cognitive sophistication in explaining these differences across the U.S.A. (N = 689), the U.K. (N = 642), and Canada (N = 644) using preregistered surveys conducted in late March, 2020. We found evidence that political polarization around COVID-19 risk perceptions, behavior change intentions, and misperceptions was greater in the U.S. than in the U.K.. However, Canada and the U.S. did not strongly differ in their level of polarization. Furthermore, in all three countries, cognitive sophistication (indexed by analytic thinking, numeracy, basic science knowledge, and bullshit skepticism) was a negative predictor of COVID-19 misperceptions – and in fact was a stronger predictor of misperceptions than political ideology (despite being unrelated to risk perceptions or behavior change intentions). Finally, we found no evidence that cognitive sophistication was associated with increased polarization for any of our COVID-19 measures. Thus, although there is some evidence for political polarization of COVID-19 in the U.S. and Canada (but not the U.K.), accurate beliefs about COVID-19 (albeit not intentions to act) are broadly associated with the quality of one’s reasoning skill regardless of political ideology or background polarization.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".