Exploring researchers’ perspectives on authorship decision making
Bibliographic record
Abstract
CONTEXT: Authorship has major implications for researchers' careers. Hence, journals require researchers to meet formal authorship criteria. However, researchers frequently admit to violating these criteria, which suggests that authorship is a complex issue. This study aims to unpack the complexities inherent in researchers' conceptualisations of questionable authorship practices and to identify factors that make researchers vulnerable to engaging in such practices. METHODS: A total of 26 North American medical education researchers at a range of career stages were interviewed. Participants were asked to respond to two vignettes, of which one portrayed honorary authorship and the other described an author order scenario, and then to describe related authorship experiences. Data were analysed using thematic analysis. RESULTS: Participants conceptualised questionable authorship practices in various ways and articulated several ethically grey areas. Personal and situational factors were identified, including hierarchy, resource dependence, institutional culture and gender; these contributed to participants' vulnerability to and involvement in questionable authorship practices. Participants described negative instances of questionable authorship practices as well as situations in which these practices were used for virtuous purposes. Participants rationalised engagement in questionable authorship practices by suggesting that, although technically violating authorship criteria, such practices could be reasonable when they seemed to benefit science. CONCLUSIONS: Authorship guidelines portray authorship decisions as being black and white, effectively sidestepping key dimensions that create ethical shades of grey. These findings show that researchers generally recognise these shades of grey and in some cases acknowledge having bent the rules themselves. Sometimes their flexibility is driven by benevolent aims aligned with their own values or prevailing norms such as inclusivity. At other times participation in these practices is framed not as a choice, but rather as a consequence of researchers' vulnerability to individual or system factors beyond their control. Taken together, these findings provide insights to help researchers and institutions move beyond recognition of the challenges of authorship and contribute to the development of informed, evidence-based solutions.
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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 | 0.160 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.026 | 0.061 |
| Scholarly communication | 0.027 | 0.019 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".