Political Prescriptions: Three Pandemic Stories
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this article, we symmetrically explore the political underpinnings and connections of pharmaceutical drugs during the COVID-19 pandemic. We illustrate some different and shifting dynamics of expert-lay interplay, competing knowledge claims in politically charged environments, as well as actions and actors that can bring drugs to prominence. Focusing on three drugs, ivermectin, remdesivir, and Coronil, we offer three axes on which they can be apprehended within political logics: (a) ivermectin as a “populist drug” in the United States, (b) remdesivir as an “establishment drug” in the United States, and (c) Coronil as a “nationalist drug” in India. These three pharmaceuticals were politicized, and perhaps more surprising, politics became pharmaceuticalized. Trust in these treatments was intimately related to articulations of the threats posed by the pandemic and the best ways of addressing them, both manipulated politically by relatively powerful actors.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | medium |
| gpt | Science and technology studies Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Other design | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it