Bibliographic record
Abstract
As Artificial Intelligence (AI) technologies such as generative AI became more common, its use in academic settings also gained more popularity. Chat Generative Pre-trained Transformer (ChatGPT) is an AI powered chatbot developed by OpenAI. It has many benefits for scholarly publishing. However, ChatGPT and related technologies have been identified as disruptive innovations with the potential to revolutionize academia and scholarly publishing (Haque et al., 2022). ChatGPT can only benefit authors when used responsibly. There are certainly ethical issues with using ChatGPT for scholarly publishing. First of all, authorship is a major concern. There are questions about the ownership of the work generated by ChatGPT (Schönberger, 2018). Besides, there may be concerns about copyright as well. When using ChatGPT, users may find it challenging to ensure that quotes, data, or other materials from external sources comply with copyright laws and receive proper attribution (Gillotte, 2019). When the language models are trained on a massive amount of data from unknown sources, it is almost impossible to track the original source. As a result, plagiarism may arise from using ChatGPT. It is not limited to copyrighted text, but also includes paraphrasing, methods, graphics, ideas, and any other product of intelligence that belongs to another person (Gasparyan et al., 2017). With the issues raised above, it is necessary to examine the current state of transparency regarding the use of generative AI in scholarly publishing.
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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 | 0.031 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.053 | 0.031 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.076 | 0.034 |
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".