Trust Us - We Are the (Covid-19 Misinformation) Experts: A critical scoping review of expert meanings of ‘misinformation’ in the Covid era
Bibliographic record
Abstract
Background: Since the WHO declared Covid-19 a pandemic, prominent social actors and institutions have warned about the threat of misinformation, i.e., false or misleading information, calling on the public to join in a global crusade against it. However, neither the premises underlying expert claims nor the standards that experts use to identify truth from falsehood have been appraised. Objectives: We conducted a critical scoping review of the medical and social scientific literature, examining what this literature means by misinformation. Methods: We followed the framework of Arksey and O’Malley for scoping reviews informed by the critical perspective of the “what is the problem represented to be” approach to policy analysis. Medical sources were identified from PubMed and from three medical journals - the British Medical Journal, The Lancet, and the New England Journal of Medicine – and social science sources were identified from the journal Social Science & Medicine and from within refereed publications of self-identified and socially recognized “misinformation experts”, using a specified combination of search terms (e.g., “covid-19”; “misinformation”). Data were charted independently by two researchers. The protocol was registered in Open Science Frame. Findings: Across the data, we found that verifiability relied largely on the claims of socially recognized epistemic authorities to the exclusion of independent evidentiary standards. Further, “epistemic authority” did not depend on subject matter expertise, but rather on a new type of “expertise” - in misinformation itself. Finally, policy solutions to the alleged threat that misinformation poses to democracy and human rights called for, paradoxically, suppressing information unverified, and debate unmanaged, by experts, in the name of protecting democracy and rights. Notably, we identified no pockets of resistance to these dominant meanings and uses. Conclusions: We assess the implications of our findings for democratic public policy, and for fundamental rights and freedoms. The study is part of a larger project examining geopolitics, medicalization, and social control in the Covid era.
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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 | 0.230 | 0.531 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.035 | 0.021 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".