Toward the Transparent Use of Generative Artificial Intelligence in Academic Articles
Bibliographic record
Abstract
With the breakthrough development of generative artificial intelligence (AI), its usage in academic articles is rapidly increasing, and the risk of the lack of research transparency arises with that use. To address this risk, the sources, mechanisms, and quality of AI-generated scholarly content are studied to calibrate our expectations for this technology. The authors find that generative AI has great potential to improve the efficiency of researchers and to enhance research articles but also has significant inherent limitations. Then, they examine the use of generative AI in academic articles from three perspectives: AI-assisted research issue development, AI-assisted addressing of research questions, and AI-assisted research findings communication. On this basis, the authors propose a tiered disclosure strategy based on the generative AI usage context for researchers to transparently use generative AI.
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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 | 0.404 | 0.630 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.011 | 0.042 |
| Scholarly communication | 0.045 | 0.046 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".