“You are not a horse”: Medicalization, social control, and academic discourse in the Covid-19 era
Bibliographic record
Abstract
Since early 2020, public figures in government, medicine, public health, and academia have accused critics of official Covid-19 policy of subverting efforts to contain the crisis, by spreading “misinformation” leading to concerning levels of “vaccine hesitancy” or to the uptake of unproven, even dangerous, therapeutics. These accusations were compellingly captured in an August 2021 tweet from the US Food and Drug Administration (FDA), “You’re not a horse. You are not a cow. Seriously, y’all. Stop it”, warning anyone considering or already consuming the antiparasitic drug ivermectin to treat or prevent Covid-19 that the drug could be “dangerous and even lethal” if used outside of the scope of FDA guidelines. In this study I examine the role of academic popularizing discourse in Covid-19 debates. Drawing from theories and methods that share a concern with how medical language and frames are deployed to control social behaviour, I appraise articles from The Conversation, an outlet that disseminates academic knowledge to facilitate open exchange and democratic governance. My analysis challenges the outlet’s self-presentation, suggesting instead that, in the Covid-19 era, far from contributing to its ostensible goals, The Conversation’s stigmatizing and condemnatory messaging is largely undermining them, with dire implication for the normative academic principles of open inquiry, the free pursuit of knowledge, and the promotion of critical thinking among younger generations.
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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 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.031 | 0.138 |
| Scholarly communication | 0.041 | 0.028 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".