Combating corruption in the pharmaceutical arena
Bibliographic record
Abstract
Corruption in healthcare generally and specifically in the pharmaceutical arena has recently been highlighted in reports by Transparency International. This article focuses on four areas of corruption: legislative/regulatory, financial, ideological/ethical, and communications. The problems identified and the solutions considered focus on structural considerations affecting how pharmaceuticals are discovered, developed, distributed, and ultimately used in clinical settings. These include recourse to user fees in the regulatory sphere, application of intellectual property rights to medical contexts (patents and access to research data), commercial sponsorship of ghost writing and guest authors, linkage/delinkage of the funding of research and overall health objectives to/from drug pricing and sales, transparency of payments to healthcare professionals and institutions, and credible regulatory sanctions. In general, financial and other incentives for all actors in the system should be structured to align with desired social outcomes - and to minimise conflicts of interest among researchers and clinicians.
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 | 0.034 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".