Bibliographic record
Abstract
Efforts to foster transparency in biopharmaceutical regulation are well underway: drug manufacturers are, for example, legally required to register clinical trials and share research results in the United States and Europe. Recently, the policy conversation has shifted toward the disclosure of clinical trial data, not just trial designs and basic results. Here, I argue that clinical trial registration and disclosure of clinical trial data are necessary but insufficient. There is also a need to ensure that regulatory decisions that flow from clinical trials - whether positive (i.e., product approvals) or negative (i.e., abandoned products, product refusals, and withdrawals) - are open to outside scrutiny. Further, a jurisprudence of drug regulation is needed. I develop two arguments motivated by (1) innovation concerns and (2) the value of good governance in support of openly publishing all final decisions for approved, abandoned, refused, and withdrawn products. After articulating why greater transparency in regulatory decision-making is needed, I distil four essential features of a jurisprudence of drug regulation that prescribe policy changes in terms not only of the transparency of regulatory outcomes and the underlying reasoning, but also regulatory organization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.100 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.095 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.035 | 0.048 |
| Insufficient payload (model declined to judge) | 0.003 | 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".