Against vaccine assay secrecy
Bibliographic record
Abstract
Increasing the transparency of the evidence base behind health interventions such as pharmaceuticals, biologics, and medical devices, has become a major point of critique, conflict, and policy focus in recent years. Yet the lack of publicly available information regarding the immunogenicity assays upon which many important, widely used vaccines are based has received no attention to date. In this paper we draw attention to this critical public health problem by reporting on our efforts to secure vaccine assay information in respect of 10 vaccines through Canada's access to information law. We argue, under Canadian law, that the public health interest in having access to the methods for these laboratory procedures should override claims by vaccine manufacturers and regulators that this information is proprietary; and, we call upon several actors to take steps to ensure greater transparency with respect to vaccine assays, including regulators, private firms, researchers, research institutions, research funders, and journal editors.
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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 | 0.223 | 0.369 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.027 | 0.038 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".