A Platform (Authorships.org) for the Objective Qualification and Order of Academic Authorship in Medical and Science Journals: Development and Evaluation Study Using the Design Science Research Methodology
Bibliographic record
Abstract
BACKGROUND: The qualification and order of authorship in scientific manuscripts are the main disputes in collaborative research work. OBJECTIVE: The aim of this project was to develop an open-access web-based platform for objective decision-making of authorship qualification and order in medical and science journals. METHODS: The design science process methodology was used to develop suitable software for authorship qualification and order. The first part of the software was designed to differentiate between qualification for authorship versus acknowledgment, using items of the recommendations of the International Committee of Medical Journal Editors. The second part addressed the order of authorship, using the analytical hierarchy process for objective multiple criteria decision-making and ranking. The platform was evaluated qualitatively (n=30) and quantitatively (n=18) using a dedicated questionnaire, by an international panel of medical and biomedical professionals and research collaborators worldwide. RESULTS: Authorships.org represents an open-access software compatible with all major platforms and web browsers. Software usability and output were evaluated and presented for 3 existing clinical and biomedical research studies. All 18 international evaluators felt that the Authorships.org platform was easy to use or remained neutral. Moreover, 59% (n=10) were satisfied with the software output results while the rest were unsure, 59% (n=10) would definitely use it for future projects while 41% (n=7) would consider it, 94% (n=16) felt it may prove useful to eliminate disputes regarding authorship, 82% (n=14) felt that it should become mandatory for manuscript submission to journals, and 53% (n=9) raised concerns regarding the potential unethical use of the software as a tool. CONCLUSIONS: Authorships.org allows transparent evaluation of authorship qualification and order in academic medical and science journals. Objectified proof of authorship contributions may become mandatory during manuscript submission in high-quality academic journals.
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.
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 | 0.091 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".