Bibliographic record
Abstract
Inappropriate authorship is a common problem in biomedical research and may be becoming one in bioethics, due to the increase in multiple authorship. This paper investigates the authorship policies of bioethics journals to determine whether they provide adequate guidance for researchers who submit articles for publication, which can help deter inappropriate authorship. It was found that 63.3% of bioethics journals provide no guidance on authorship; 36.7% provide guidance on which contributions merit authorship, 23.3% provide guidance on which contributions do not merit authorship, 23.3% require authors to take responsibility for their contributions or for the article as a whole, 20% provide guidance on which contributions merit an acknowledgement but not authorship, 6.7% require authors to describe their contributions, and only 3.3% distinguish between authorship in empirical and conceptual research. To provide authors with effective guidance and promote integrity in bioethics research, bioethics journals should adopt authorship policies that address several important topics, such as the qualifications for authorship, describing authorship contributions, taking responsibility for the research and the difference between authorship in empirical and conceptual research.
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 | 0.063 | 0.262 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".