Bibliographic record
Abstract
In recent years, seemingly frequent examples of problems related to the integrity of authorship and publication have plagued the medical literature. These issues have been primarily related to the appropriateness of authorship (including ghostwriting and so-called “guest authorship” of manuscripts), duplicate publication of articles, plagiarism, scientific miscon duct in the form of falsification of data, and failure to disclose conflicts of interest. Although authorship and publication issues arise relatively rarely at the CJHP , the Journal has, on occasion, been faced with some of these concerns. Two issues that have received attention recently in the scientific literature and even in the lay press are the ghostwriting and guest authorship of scientific articles. Ghostwriting has been defined as “the failure to designate an individual (as an author) who has made a substantial contribution to the research or writing of a manuscript.” 1 Particular attention was drawn to this practice in a review of industry documents obtained during litigation related to rofecoxib, 1 in which it was discovered that numerous review articles had been prepared by people who were not recognized as authors or otherwise acknowledged. Instead, the authorship of these papers was attributed to investigators with academic affiliations. This review also revealed that many clinical trial manuscripts were written primarily by industry employees, with first authorship on each paper being attributed to an investigator with an aca demic affiliation. 1
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 | 0.377 | 0.679 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.010 | 0.054 |
| Scholarly communication | 0.043 | 0.029 |
| Open science | 0.009 | 0.022 |
| Research integrity | 0.021 | 0.026 |
| Insufficient payload (model declined to judge) | 0.021 | 0.024 |
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".