STAKEHOLDER ENGAGEMENT IN PHARMACEUTICAL REGULATION: CONNECTING TECHNICAL EXPERTISE AND LAY KNOWLEDGE IN RISK MONITORING
Bibliographic record
Abstract
The exclusive position of scientific expertise in pharmaceutical regulation is being increasingly challenged. Several authors suggest that lay knowledge could play a role in governing risks. We use the literature to develop ideal‐typical regulatory arrangements with low and high lay stakeholder involvement: a technocratic and a democratic arrangement. We propose that a more technocratic arrangement will yield a better process and output performance while a more democratic arrangement will result in more stakeholder satisfaction. These propositions are explored through two case studies of pharmaceutical regulation in the Netherlands: in pandemic influenza and in HIV. Our study shows equivalent process and output performances but we found indications that the democratic approach results in more stakeholder satisfaction. We conclude that in pharmaceutical regulation, there is no a priori reason to limit involvement to experts: in situations of fundamental uncertainty, democratic monitoring of pharmaceutical risks can contribute to the system's robustness.
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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 | 0.051 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".