A Rapid Investigation of Artificial Intelligence Generated Content Footprints in Scholarly Publications
Bibliographic record
Abstract
This study reports on a novel phenomenon observed in scholarly publications. Some research articles unrelated to the field of artificial intelligence (AI)–generated content (AIGC) contain phrases such as ‘As an AI language model …’ The authors conceptualize this phenomenon as ‘AIGC footprints.’ To provide early evidence, they conducted a small-scale sample investigation by collecting twenty-five articles. These articles were published by authors from countries in Central Asia, South Asia, and Africa. Among these authors, there were assistant professors, doctoral and master’s students. Single authors and single affiliations were more common. Analysis of the article content revealed that some authors utilized ChatGPT for literature reviews or idea generation. The twenty-five articles with AIGC footprints were published in eighteen different academic journals. The emergence of AIGC footprints reflects the potential challenges faced by scholarly publishing and higher education. The authors also provide several recommendations.
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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 | 0.021 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".