Public Risk-Taking and Rewards During the COVID-19 Pandemic - A Case Study of Remdesivir in the Context of Global Health Equity
Bibliographic record
Abstract
Public investment, through both research grants and university funding, plays a crucial role in the research and development (R&D) of novel health technologies, including diagnostics, therapies, and vaccines, to address the coronavirus disease 2019 (COVID-19) pandemic. Using the example of remdesivir, one of the most promising COVID-19 treatments, this paper traces back public contributions to different stages of the innovation process. Applying the Risk-Reward Nexus framework to the R&D of remdesivir, we analyse the role of the public in risk-taking and reward and address inequities in the biomedical innovation system. We discuss the collective, cumulative and uncertain characteristics of innovation, highlighting the lack of transparency in the biomedical R&D system, the need for public investment in the innovation process, and the "time-lag" between risk-taking and reward. Despite the significant public transnational contributions to the R&D of remdesivir, the rewards are extracted by few actors and the return to the public in the form of equitable access and affordable pricing is limited. Beyond the necessity to treat remdesivir as a global public good, we argue that biomedical innovation needs to be viewed in the broader concept of public value to prevent the same equity issues currently seen in the COVID-19 pandemic. This requires the state to take a market-shaping rather than market-fixing role, thereby steering innovation, ensuring that patents do not hinder global equitable access and affordable pricing and safeguarding a global medicines supply.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".