How populism and conservative media fuel conspiracy beliefs about COVID-19 and what it means for COVID-19 behaviors
Bibliographic record
Abstract
Research examining attitudes and behaviors of Americans during the COVID-19 pandemic has largely focused on partisanship as a lens through which many Americans see the coronavirus. Given the importance of partisan affiliation and the degree of partisan polarization in the American society, that is certainly an important driver of public opinion, and a necessary one to understand. But an overlooked set of predispositions might also shape COVID beliefs and attitudes: populism. It is a worldview that pits average citizens against “the elites” and, importantly in the context of a pandemic, it includes anti-intellectual attitudes such as distrust of experts (including scientists). We find that populism is correlated with conspiracy beliefs about COVID-19, above and beyond partisanship. Furthermore, we find that conservative media consumption tends to be a stronger predictor of conspiracy belief among those high in populism than among those low in populism. We also show that these beliefs have consequences: those who believe the conspiracy theories about COVID-19 are less likely to adapt behaviors recommended by public health officials.
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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 | 0.001 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".