Zero Tolerance to Plagiarism in Multicultural Teamwork: Challenges for English-Speaking non-EU and EU Academics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 The most immediate consequence of plagiarism is the inundation of outdated scientific information with articles that imitate scientific activity, making it challenging to discover genuinely novel scientific information even with the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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it