Contemplating and Visualizing Plagiarism Through a Bibliometric Study
Bibliographic record
Abstract
Plagiarism has become a buzz word in higher education sector graving as unlawful act in academic publishing. A lot of literature covering the various aspects of plagiarism and related issues has been published in the form of scientific communications. The article contemplates and visualizes the literature on plagiarism with the help of selective bibliometric parameters. The present study is focused upon concept of plagiarism with an intend to enrich the quality of research and bringing in more awareness on the topic by presenting detailed analysis on the quantum of research work from 1989-2022. During the study period, 3771 validated documents were found on the theme of plagiarism. An average citation per document is registered as 14.3. Wiwanikit, V; Rolg, M; Joob, B, and Marusic A had the highest publications on plagiarism literature. The analysis in the given study stated that USA, United Kingdom, China, Australia, and Canada have been most contributing countries in terms of the research output. Using Bradford’s law, the top ten sources along with SJR value13from the core zone has been evaluated. The conceptual structure on theme of plagiarism is revealed through co-occurrence of keyword and thematic map. ‘Plagiarism’, academic dishonesty’ and ‘attitude’ are found to be highly occurred keywords.The article written by Drummond, G. B. entitled ‘Reporting ethical matters in The Journal of Physiology: standards and advice’ is found to be highly cited in overall output of plagiarism literature. The stakeholder of this research would be benefited from the quantitative information on the theme of plagiarism. The list of prominent authors, core journals and multitude themes will help them to submerge in the subject of plagiarism to explore more the issue related it
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.100 | 0.099 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".