Biopharmaceutical Financialization and Public Funding of Medical Countermeasures (MCMs) in Canada During the COVID-19 Pandemic
Bibliographic record
Abstract
Background: Analysing the Canadian government’s efforts to support the development of COVID-19 "medical countermeasures" (MCMs), this article seeks insights into political economy as a driver of pandemic response. We explore whether Canadian public funding policy during the pandemic involved departures from established practices of financialisation in biopharmaceutical research and development (R&D), including the dominance of private sector involvement in an intellectual property (IP) intensive approach to innovation underscoring profit, and governance opacity. Methods: We interrogate public funding for MCMs by analyzing how much the Government of Canada (GoC) spent, how those funds were allocated, on what terms, and to whom. We identify the funding institutions, and the funds awarded between February 10, 2020, and March 31, 2021, to support the research, development, and manufacturing of MCMs, including diagnostics, vaccines, therapeutics, and information about clinical management and virus transmission. To collect these data, we conducted searches on the Internet, public data repositories, and filed several requests under the Access to Information Act (1985). Subsequently, we carried out a document-based analysis of electronically accessible research contracts, proposals, grant calls, and policy announcements. Results: The GoC announced CAD$ 1.4 billion for research, development and manufacturing of COVID-19 MCMs. Fully 68% (CAD$ 959 million) of the announced public funding was channelled to investment in private sector firms. Canadian public funding showed a consistent focus on early and late stage development of COVID-19 MCMs and the expansion of biopharmaceutical manufacturing capacity. Assessing whether Canada’s investments into developing COVID-19 MCMs safeguard affordable and transparent access to the products of publicly funded research, we found that access policies on IP management, sharing of clinical data, affordability and availability were not systematic, consistent, or transparent, and few, if any, mechanisms ensured long-term sustainability. Conclusion: Beyond incremental change in policy goals, such as public investment in domestic biomanufacturing, the features of Canadian public policies endorsing financialization in the biopharmaceutical sector remained largely unchanged during the pandemic.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".