Zero Tolerance to Plagiarism in Multicultural Teamwork: Challenges for English-Speaking non-EU and EU Academics
Bibliographic record
Abstract
The paper discusses scientific communication and notes that the primary means is through scientific literature, which serves as a vessel for circulating knowledge and information about the world around us. However, in today's post-academic scientific landscape, where the number of publications in international databases is the main yardstick for assessing the productivity of scientists, research, and educational institutions, the issue of plagiarism in scientific communications has become increasingly relevant. It's worth noting that scientific articles are recognized as the primary form of communication, while other types of scientific publications such as monographs, abstracts in collections, and conference proceedings, which constitute a significant portion of modern scientific communication, are often overlooked. It has been shown that in Ukraine and EU countries where scientists from different nationalities and cultures participate, the objective isn't to eradicate plagiarism as a deviation from morality and law, but rather to significantly decrease its prevalence in science and higher education by addressing the factors that contribute to it. The most immediate consequence of plagiarism is the inundation of outdated scientific information with articles that imitate scientific activity, making it challenging to discover genuinely novel scientific information even with the assistance of the internet. Plagiarism also devalues the significance of scientific publications, complicates the identification of truly valuable publications, and violates the ethical and legal norms of scientific activity and scientific communication.
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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 | 0.095 | 0.242 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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, 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".