Building the social problem of the infodemic in Brazil: analysis of discursive formations used in media coverage on COVID-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it