Pandemic activism in comparative perspective: exploring the roles of populist attitudes, direct online political sources, and misinformation
Bibliographic record
Abstract
Managing the COVID-19 pandemic required governments to act quickly with little input from legislatures, civil society, and citizens. This crisis created a critical opportunity for populists to fuel distrust in media, government, and science. Using digital media, they and other political leaders can communicate directly with citizens through websites and social media accounts. Furthermore, misinformation on social media circulating during the pandemic created further opportunities to generate support for populist ideas and motivate collective action in reaction to public health measures. This paper examines the roles of populist attitudes, direct online political sources, misinformation, and pandemic activism using a survey conducted in January and February 2023 in Germany, Canada, the UK, the US, and France. We find that perceived exposure to misinformation, self-assessed ability to detect misinformation, and sharing of misinformation positively correlate with populist attitudes and pandemic activism. Sharing misinformation relates to pandemic activism in all five countries and all ideological groups. Consuming direct online sources of information from candidates and parties, such as on websites and social media, is weakly related to populist attitudes but highly correlated with pandemic activism. Specifically, visiting candidates’ or parties’ websites significantly correlates with pandemic activism; this relationship is significant in all countries and ideological groups. Finally, we find a weak relationship between holding populist attitudes and participating in pandemic activism; this relationship is only significant in Canada, France, and Germany. We offer important insights into cross-national differences in pandemic activism. We conclude with a discussion of the implications of misinformation-motivated activism.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".