Medical publishing and the drug industry: is medical science for sale?
Bibliographic record
Abstract
ABSTRACT The pharmaceutical industry produces an abundance of special‐purpose knowledge, flooding the markets it is most interested in. To gain the largest scientific impact and market value from research, drug company articles placed in medical journals are often written under the names of independent medical researchers. Pharmaceutical company statisticians, reviewers from a diverse array of company departments, medical writers, and publication planners are only rarely acknowledged in journal publications, and key company scientists only sometimes acknowledged. The public knowledge that results from this ghost‐managed research and publication is a marketing tool, providing bases for continuing medical education, buttressing sales pitches, and contributing to medical common sense and further research. In the pharmaceutical industry, knowledge is a resource to be accumulated, shaped, and deployed to best effect. In this paper, I describe this process and discuss ways in which it might be addressed.
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 | 0.018 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.038 | 0.029 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.041 | 0.007 |
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".