Political Preferences, Knowledge, and Misinformation About COVID-19: The Case of Brazil
Bibliographic record
Abstract
The COVID-19 pandemic has led to a vast research agenda focusing on how citizens acquire knowledge about the virus and the health expert guidelines to protect themselves and their close ones against it. While many countries and regions have been accounted for, there still remains a substantial gap with respect to public opinion about the virus in Latin America, most notably in Brazil, which currently has the second highest in number of fatalities in the world. In this article, we employ a national survey of Brazilians (n = 2,771) to measure and explain knowledge and misinformation about the coronavirus and its illness, COVID-19. Our focus concerns the role of political preferences in a context of high elite polarization with a sitting government that has systematically downplayed the risks associated with the coronavirus and its illness. Our findings are clear: political preferences play a substantial role in explaining differences in knowledge about the coronavirus and COVID-19, more than conventional determinants of learning like motivation, ability, and opportunities. Specifically, we find that supporters of President Jair Bolsonaro—an avid science and COVID-19 denier—know significantly less about the coronavirus and its illness and are more likely to believe in a conspiracy theory that claims that the coronavirus was purposefully created in a Chinese laboratory to promote China's economic power, when compared to Brazilians who are less supportive of him and his government. Our findings carry important implications for how Brazilians take informational cues from political elites in that—even in a major event like a global pandemic—supporters of the president are as likely as ever to “follow their leader” and deny expert-backed scientific evidence.
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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 | 0.003 | 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 | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".