Honorary authorship in biomedical journals: how common is it and why does it exist?
Bibliographic record
Abstract
BACKGROUND: The number of coauthors in the medical literature has increased over the past 50 years as authorship continues to have important academic, social and financial implications. AIM AND METHOD: The study aim was to determine the prevalence of honorary authorship in biomedical publications and identify the factors that lead to its existence. An email with a survey link was sent anonymously to 9283 corresponding authors of PubMed articles published within 1 year of contact. RESULTS: A completed survey was obtained from 1246 corresponding authors, a response rate of 15.75%. One-third (33.4%) admitted that they had added authors who did not deserve authorship credit. Origin of the study from Europe and Asia (p ≤ 0.001 and 0.005, respectively), study type as case report/case series (p=0.036) and increasing number of coauthors were found to be the associated factors on multivariate analysis. Journal impact factor was also found to be associated with honorary authorship (mean journal impact factor was 4.82 (SD 6.32) for those who self-reported honorary authorship and 5.60 (SD 7.13) for those who did not report unjust authorship, p=0.05). In retrospect, 75% of the authors indicated that they would remove unjustified names from the authorship list. Reasons for adding honorary authors were complimentary (39.4%), to avoid conflict at work (16.1%), to facilitate article acceptance (7.2%), and other (3.6%). CONCLUSIONS: Honorary authorship is relatively common in biomedical publications. Researchers should comply with the International Committee of Medical Journal Editors' criteria for authorship.
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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 | 0.016 | 0.147 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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; 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".