Bibliographic record
Abstract
Many scholarly publishers still don’t have policies on whether researchers can submit papers written by artificial intelligence chatbots like ChatGPT . Jeremy Y. Ng, a metascientist who works at the Centre for Journalology at the Ottawa Hospital Research Institute, and colleagues audited the publicly available policies of 163 members of the International Association of Scientific, Technical, and Medical Publishers (STM). Of those 163 members, only 56 had a policy on whether authors can submit papers written by AI chatbots (medRxiv 2024, DOI: 10.1101/2024.06.19.24309148 ). Forty-nine of those 56 publishers required authors to declare when they used chatbots, and none allowed researchers to list AI tools like ChatGPT as an author. “The use of AI chatbots in academic publishing is a new and rapidly evolving space,” Ng says. “The absence of industry-wide standards or guidelines also contributes to the slow adoption.” The American Chemical Society and the Royal Society of Chemistry,
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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 | 0.163 | 0.410 |
| Meta-epidemiology (narrow) | 0.001 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.042 | 0.049 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.080 | 0.111 |
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".