Predatory Journals and AI-Assisted Plagiarism of Scientific Research: Bootlegging Research as a New Criminological Reality
Bibliographic record
Abstract
In 2025, the first known case of what appears to be an article generated by artificial intelligence that plagiarized a CJCCJ article was reported to the journal and its publisher. Such fraudulent bootlegging practices challenge the legitimacy of scientific research and the peer-review process, while raising a number of critical issues with the rapid rise of generative artificial intelligence and large language models (LLMs). This article presents an overview of this new phenomenon of AI-assisted plagiarism, a set of concerning issues that the scientific community is currently struggling with. While LLMs are unlikely to go away any time soon, currently available tools to detect AI-assisted plagiarism of legitimate research are not adequate and mechanisms to prevent it are non-existent. In that content, we speculate that this case could be a first of many more to come that will impact not only criminology but also other academic disciplines.
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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 | 0.054 | 0.222 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.012 | 0.040 |
| Scholarly communication | 0.027 | 0.020 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".