Bibliographic record
Abstract
In recent days, there has been a lot of discussion about plagiarism in higher education. Students may utilise Artificial Intelligence (AI) technologies like ChatGPT (Chat Generative Pre-trained Transformer) and chatbots to produce answers to use in their academic writing. The growth of artificial intelligence (AI) chatbot technology and its impact on education is a trending topic, and especially ChatGPT has sparked worries among scholars. The main objective of this study is to discover publication trends and to realize the network visualisation of the co-occurrence of keywords, co-authorship of countries, citation and co-citation of authors and countries, and bibliographic coupling analysis in the context of plagiarism. This study used the bibliometric analysis method. The Web of Science was used to extract publication data. The word “plagiarism” is used to search the literature, and we found 3282 publications published between 1989 and 2023. VOSviewer software is used to visualize bibliometric networks of publications. Results show that the highest amount of research was produced in 2019, and the number of publications increased rapidly. The United States of America (USA), the United Kingdom (UK), China, Australia, and Canada contributed the most publications. Elsevier, Springer, Taylor & Francis, Wiley, and Sage are top publishers that produce a large number of publications on plagiarism. This analysis gives a comprehensive perspective on plagiarism research for scholars, which will also be useful for educators, educational institutions, and publishers.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Research integrity Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Research integrity Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.007 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.047 | 0.056 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".