Competitive interdependence: A critical political economy of regulation during the COVID-19 pandemic
Bibliographic record
Abstract
This article investigates Canada's health product regulatory authority, Health Canada (HC), and other major national regulators' responses to COVID-19. Drawing on semi-structured interviews with HC officials and secondary data on the activities of other major regulators, including the European Medicines Agency, the United Kingdom's Medicines and Healthcare Products Regulatory Agency, and the United States Food and Drug Administration, we show that during COVID-19 product evaluations, HC and other regulatory authorities adopted a strategy of increased collaboration and competition with one another. We term this strategy a pattern of 'competitive interdependence.' Using a critical political economy (CPE) approach, we argue that regulatory authorities employed the strategy to mediate increased structural tensions between capitalism and democracy engrained in health product regulation. The CPE approach, informing our analysis of competitive interdependence, highlights the dialectical nature of health product regulation. In light of our data, we demonstrate the regulators' role in upholding capitalism at both the national and global levels while also organizing popular consent by generating public trust in the safety and efficacy of medicines.
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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 | 0.036 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.064 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".