Human‐ and <scp>AI</scp>‐based authorship: Principles and ethics
Bibliographic record
Abstract
Key points The International Committee of Medical Journal Editors (ICMJE) recommendations for authorship are the dominant guidelines that guide who, and under what circumstances, an individual can be an author of an academic paper. Large language models (LLMs) and AI, like ChatGPT, given their ability and versatility, pose a challenge to the human‐based authorship model. Several journals and publishers have already prohibited the assignment of authorship to AI, LLMs, and even ChatGPT, not recognizing them as valid authors. We debate this premise, and asked ChatGPT to opine on this issue. ChatGPT considers itself as an invalid author. We applied the CRediT criteria to AI, finding that it was definitively able to satisfy three out of the 14 criteria, but only in terms of assistance. This was validated by ChatGPT itself.
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How this classification was reachedexpand
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: Reporting · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
| gpt | Research integrityScholarly communication Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.155 | 0.427 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.019 | 0.022 |
| Insufficient payload (model declined to judge) | 0.012 | 0.013 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".