Leaky Vaccines: A Wicked Problem in Accelerated Vaccine Development
Bibliographic record
Abstract
While no vaccine can provide 100 percent protection, a high standard of regulatory safety, efficacy and quality is essential for public trust and the uptake of vaccines as essential global public health tools. This article addresses a growing concern that suboptimal “leaky” vaccines threaten emergency response to pandemics as well as routine public health programs. Agile regulatory standards now advance earlier approval of vaccines and therapeutics that may have suboptimal effectiveness. The benefit-harm trade- offs that play an enormous role in regulatory assessment and all stages of vaccine development and delivery deserve better public scrutiny, transparency, and accountability. Drawing on the case of the first licensed malaria vaccine, RTS,S Mosquirix™, in light of the rapid approval of COVID-19 vaccines, we consider the socio-technical implications of leaky vaccines in global vaccine logics and suggest possibilities for building legitimacy to inform the next generation of regulatory technology policy.
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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 | 0.057 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.084 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".