Postplagiarism: transdisciplinary ethics and integrity in the age of artificial intelligence and neurotechnology
Bibliographic record
Abstract
Abstract In this article I explore the concept of postplagiarism, loosely defined as an era in human society and culture in which advanced technologies such as artificial intelligence and neurotechnology, including brain-computer interfaces (BCIs), become a normal part of life, including how we teach, learn, communicate, and interact on a daily basis. Ethics and integrity are intensely important in the postplagiarism era when technology cannot be decoupled from everyday life. I argue that it might be reasonable to assume that when commercialized neuro-educational technology is readily available in a form that is implantable/ingestible/embeddable and invisible then academic integrity arms race will be over, as detection will be an exercise in futility. In a postplagiarism era, humans are compelled to grapple with questions about ethics and integrity for a socially just world at a time when advanced technology cannot be unbundled from education or everyday life. I conclude with a call to action for transdisciplinary research to better understand ethical implications of advanced technologies in education, emphasizing that such research can be considered pre-emptive , rather than speculative . The ethical implications of ubiquitous artificial intelligence and neurotechnology (e.g., BCIs) in education are important at a global scale as we prepare today’s students for academic and lifelong success.
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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 | 0.036 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.107 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".