Bibliographic record
Abstract
This paper explores the public sector’s often undersold role in vaccine research and development (R&D). Further, it examines the recent shift in vaccine innovation policy caused by the urgency of the COVID-19 pandemic. Due to factors such as lack of repetitive dosage, high levels of at-risk investment, and long, large clinical trials, it is difficult to incentivize the private sector to invest in vaccine R&D. To counter these difficulties, vaccine innovation policy focuses on both push and pull incentives to ensure vaccine R&D is proceeding. Government institutions make large at-risk investments to offset the risk for private sector investment into vaccine R&D. Additionally, the government creates innovation policies that make intellectual property rights (IPR) more favourable for those who invest in vaccine R&D. With the removal of IPR for COVID-19 vaccines, the public sector became more instrumental in incentivizing private sector investment, using techniques such as mass ex-ante government vaccine procurement agreements, public-private partnerships, and increased government investments. With more significant government investments in downstream development and manufacturing activities, there is concern that the basic scientific research required for advances in vaccine technology will diminish. With the COVID-19 pandemic still unfolding, it is difficult to determine whether vaccine innovation policy will remain as it is now. However, even if vaccine innovation policy shifts post-pandemic, the public sector must continue to play an important role in vaccine R&D.
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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 | 0.031 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.044 | 0.011 |
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".