Best Practice to Order Authors in Multi/Interdisciplinary Health Sciences Research Publications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Misunderstanding and disputes about authorship are commonplace among members of multi/interdisciplinary health research teams. If left unmanaged and unresolved, these conflicts can undermine knowledge sharing and collaboration, obscure accountability for research, and contribute to the incorrect attribution of credit. To mitigate these issues, certain researchers suggest quantitative authorship distributions schemes (e.g., point systems), while others wish to replace or minimize the importance of authorship by using "contributorship"-a system based on authors' self-reporting contributions. While both methods have advantages, we argue that authorship and contributorship will most likely continue to coexist for multiple ethical and practical reasons. In this article, we develop a five-step "best practice" that incorporates the distribution of both contributorship and authorship for multi/interdisciplinary research. This procedure involves continuous dialogue and the use of a detailed contributorship taxonomy ending with a declaration explaining contributorship, which is used to justify authorship order. Institutions can introduce this approach in responsible conduct of research training as it promotes greater fairness, trust, and collegiality among team members and ultimately reduces confusion and facilitates resolution of time-consuming disagreements.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Evaluation · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Research integrityScholarly communication Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.241 | 0.477 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.012 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it