PUBLICATION ETHICS AND THE GHOST MANAGEMENT OF MEDICAL PUBLICATION
Bibliographic record
Abstract
It is by now no secret that some scientific articles are ghost authored - that is, written by someone other than the person whose name appears at the top of the article. Ghost authorship, however, is only one sort of ghosting. In this article, we present evidence that pharmaceutical companies engage in the ghost management of the scientific literature, by controlling or shaping several crucial steps in the research, writing, and publication of scientific articles. Ghost management allows the pharmaceutical industry to shape the literature in ways that serve its interests. This article aims to reinforce and expand publication ethics as an important area of concern for bioethics. Since ghost-managed research is primarily undertaken in the interests of marketing, large quantities of medical research violate not just publication norms but also research ethics. Much of this research involves human subjects, and yet is performed not primarily to increase knowledge for broad human benefit, but to disseminate results in the service of profits. Those who sponsor, manage, conduct, and publish such research therefore behave unethically, since they put patients at risk without justification. This leads us to a strong conclusion: if medical journals want to ensure that the research they publish is ethically sound, they should not publish articles that are commercially sponsored.
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 | 0.205 | 0.411 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.069 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".