From discovery to delivery: public sector development of the <i>r</i>VSV-ZEBOV Ebola vaccine
Bibliographic record
Abstract
Abstract The discovery and development of the Ebola rVSV-ZEBOV vaccine challenge the common assumption that the research and development for innovative therapeutic products and vaccines is best carried out by the private sector. Using internal government documents obtained through an access to information request, we analyze the development of rVSV-ZEBOV by researchers at Canada’s National Microbiology Laboratory beyond its patenting and licensing to a biotech company in the United States in 2010. According to government documentation, the company failed to make any progress toward a phase 1 clinical trial until after the WHO Public Health Emergency of International Concern freed substantial donor and public funds for the vaccine’s further development. The development of rVSV-ZEBOV, from sponsoring early stage research through to carrying out clinical trials during the epidemic, was instead the result of the combined efforts of the Canadian government, its researchers, and other publicly funded institutions. This case study of rVSV-ZEBOV underscores the significant public contribution to the R&D of vaccines even under conditions of precarity, and suggests that an alternative approach to generating knowledge and developing interventions, such as open science, is required in order to fully realize the public sector’s contribution to improved global health.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".