Ten strategies for avoiding and overcoming authorship conflicts in academic publishing
Bibliographic record
Abstract
For better or for worse, authorship is a currency in scholarly research and advancement. In scholarly writing, authorship is widely acknowledged as a means of conferring credit but is also tied to concepts such as responsibility and accountability. Authorship is one of the most divisive topics both at the level of the research team and more broadly in the academy and beyond. At present, authorship is often the primary way to assert and receive credit in many scholarly pursuits and domains. Debates rage, publicly but mostly privately, regarding authorship. Here we attempt to clarify key concepts related to authorship informed by our collective experiences and anchored in relevant contemporary literature. Rather than dwelling on the problems, we focus on proactive strategies for creating more just, equitable, and transparent avenues for minimizing conflict around authorship and where there is adequate recognition of the entire process of knowledge generation, synthesis, sharing, and application with partners within and beyond the academy. We frame our ideas around 10 strategies that collectively constitute a roadmap for avoiding and overcoming challenges associated with authorship decisions.
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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 | 0.097 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.013 | 0.033 |
| Scholarly communication | 0.035 | 0.022 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.008 | 0.006 |
| 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".