Bibliographic record
Abstract
Industry control over the production and distribution of pharmaceutical safety and efficacy data has become a serious public health and health care funding concern. Various recent scandals, several involving the use of flawed representations of scientific data in the most influential medical journals, highlight the urgency of enhancing pharmaceutical knowledge governance. This paper analyzes why this is a human rights concern and what difference a human rights analysis can make. The paper first identifies the challenges associated with the current knowledge deficit. It then discusses, based on an analysis of case law, how various human rights associated interests can be invoked to support the claim that states have an obligation to actively contribute to independent knowledge governance, for example through ensuring clinical trials transparency. The paper further discusses a conceptual use of human rights, as a methodology which requires a comprehensive analysis of the different interwoven historical, economic, cultural, and social factors that contribute to the problem. Such an analysis reveals that historically grown drug regulations have, in fact, contributed directly to industry control over pharmaceutical knowledge production. This type of finding should inform needed reforms of drug regulation. The paper ends with a recommendation for a comprehensive global response to the problem of pharmaceutical knowledge governance.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".