STAKEHOLDER ENGAGEMENT IN PHARMACEUTICAL REGULATION: CONNECTING TECHNICAL EXPERTISE AND LAY KNOWLEDGE IN RISK MONITORING
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it