Building the social problem of the infodemic in Brazil: analysis of discursive formations used in media coverage on COVID-19
Bibliographic record
Abstract
This article discusses the meanings of scientific misinformation in journalistic discourse over the first two years of the Covid-19 pandemic in Brazil. It analyzes the media’s role in raising public awareness about the negative social effects of scientific misinformation by mediating the debate between different claimants interested in the issue. Based on a constructivist sociology of social problems and a sociology of journalism approach, this study conducts a discourse analysis of 40 articles published in three media. The focus is on the discursive formations used by these media outlets to construct infodemic as a social problem, returning to the operations of naming, blaming, and claiming this issue. Findings suggest that journalism frames scientific misinformation as a social problem on the public agenda by using specific discursive formations in which infodemic is presented as a Manichaean view of the issue, pitting those who spread fake news against those who produce ‘true’ discourse. The study highlights the role of journalism in this debate, denouncing misinformation as a strategy to defend its professional expertise. In addition, the media analyzed have denounced fake news in science to produce public criticism against sectors associated with the group of then-president Bolsonaro (2019–2022).
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".