A “disorder that exacerbates all other crises” or “a word we use to shut you up”? A critical policy analysis of NGOs’ discourses on COVID-19 misinformation
Bibliographic record
Abstract
Abstract Drawing from Carol Bacchi’s “What is the problem represented to be?” framework, we analysed discourses within documents from five nongovernmental organizations (NGOs) that have influenced the debate around “COVID-19 misinformation.” Through Google Scholar, we identified documents published between 2020 and 2024, selecting 29 reports by the Center for Countering Digital Hate, First Draft, Meedan, Public Good Projects, and the EU Disinfo Lab, and 13 articles authored by their directors or research directors. Across the data, the proposed policy solutions consisted of tracking, managing, and suppressing any COVID-relevant expression perceived as undermining the official policy response. It followed that the “problem” was represented to be these expressions, or rather, individuals producing them, framed as threatening science, democracy, and even human survival. NGOs also positioned themselves as experts in an emerging scientific field, infodemiology, thus equipped to evaluate all forms of communication according to their own or similar experts’ standards. Notably, none of the documents engaged with the substance of opposing viewpoints or disconfirming evidence, dismissing them almost entirely via authority, ad populum, or ad hominem fallacies. We conclude that, rather than defending science, democracy, or human survival, these NGOs and their partners are undermining the open, respectful, and inclusive debate essential to support these values.
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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 | 0.053 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.024 | 0.094 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".