A Theoretical Foundation for the Ethical Distribution of Authorship in Multidisciplinary Publications
Bibliographic record
Abstract
In academia, authorship on publications confers merit as well as responsibility. The respective disciplines adhere to their "typical" authorship practices: individuals may be named in alphabetical order (e.g., in economics, mathematics), ranked in decreasing level of contribution (e.g., biomedical sciences), or the leadership role may be listed last (e.g., laboratory sciences). However, there is no specific, generally accepted guidance regarding authorship distribution in multidisciplinary teams, something that can lead to significant tensions and even conflict. Using Scanlon's contractualism as a basis, I propose a conceptual foundation for the ethical distribution of authorship in multidisciplinary teams; it features four relevant principles: desert, just recognition, transparency, and collegiality. These principles can serve in the development of a practical framework to support ethical and nonarbitrary authorship distribution, which hopefully would help reduce confusion and conflict, promote agreement, and contribute to synergy in multidisciplinary collaborative research.
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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 | 0.066 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.073 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".