What do experts mean by “misinformation” in the COVID-19 era? A critical scoping review protocol
Bibliographic record
Abstract
In April 2020, the World Health Organization released the report Managing the COVID-19 infodemic: A call to action, declaring that “the 2020 pandemic of Coronavirus disease (COVID-19) [had] been accompanied by a massive ‘infodemic.’” Soon afterwards UN Secretary General Antonio Guterres tweeted - also alluding to COVID-19 - that “a tsunami of misinformation, scapegoating and scaremongering [had] been unleashed” also in relation to COVID-19. The tweet was followed by a March 2021 report from the Centre for Health Security at the Johns Hopkins Bloomberg School of Public Health, warning that “health-related misinformation and disinformation” were undermining the public response to COVID-19, and by a February 2022 US Department of Homeland Security infographic, Disinformation Stops With You”, alerting about the dangers of “misinformation”, “disinformation”, and “malinformation” – dubbed MDM - distinguishing these terms based on the presumed intentionality of the agents producing or spreading them. However, there has been scant interrogation of expert meanings of MDM in the COVID-19 context and of the implications of the premises underlying these meanings for public policy, equity, and civil, social, and political rights. Drawing from the traditions of critical policy, discourse, and document analysis, we will apply Arksey O’Malley’s framework, enhanced by Levac et al.’s team-based approach, to conduct a critical scoping review of the medical and social scientific peer-reviewed literature, identifying, summarizing, and appraising expert meanings of MDM. We will also assess the implications of our findings for the health and well-being of populations affected by policies informed by dominant concepts of MDM.
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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 | 0.338 | 0.427 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.035 | 0.024 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.047 | 0.011 |
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".