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
Since the WHO declared COVID-19 a pandemic, prominent social actors and institutions have warned about the threat of misinformation, calling for policy action to address it. However, neither the premises underlying expert claims nor the standards to separate truth from falsehood have been appraised. We conducted a scoping review of the medical and social scientific literature, informed by a critical policy analysis approach, examining what this literature means by misinformation. We searched academic databases and refereed publications, selecting a total of 68 articles for review. Two researchers independently charted the data. Our most salient finding was that verifiability relied largely on the claims of epistemic authorities, albeit only those vetted by the establishment, to the exclusion of independent evidentiary standards or heterodox perspectives. Further, “epistemic authority” did not depend necessarily on subject matter expertise, but largely 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 suppressing unverified information and debate unmanaged by establishment approved experts, in the name of protecting democracy and rights, contrary to democratic practice and respect for human rights. Notably, we identified no pockets of resistance to these dominant meanings and uses. We assessed the implications of our findings for democratic public policy, and for fundamental rights and freedoms.
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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 | 0.102 | 0.338 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.030 | 0.020 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| 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".