Means of Forming a Culture of Academic Integrity of Postgraduate Students: Experience of Ukraine and the European Union
Bibliographic record
Abstract
The purpose of the article is to study how to form a culture of academic integrity of postgraduate students based on the consideration and analysis of the Ukrainian and European experience. To achieve this goal, the author used such research methods as analysis and synthesis, and the content analysis method was applied to study the scientific literature to official documents. The results show that the EU countries use a variety of tools, including the formation of educational ethics, university codes of conduct, specialised seminars and training, mentoring, the use of digital tools to detect plagiarism in dissertations, the imposition of severe sanctions for violations, as well as the use of public influence methods and the work of special control and accreditation commissions. The advantages of postgraduate education in European countries in terms of building academic integrity are the fact that this process has a long history of application and uses proven methods, while in Ukraine this concept is relatively new. This indicates the existence of certain weaknesses. Ukrainian codes of ethics for higher education institutions have a limited impact, unlike in European countries, where violations of academic integrity lead to the automatic isolation of a person in the scientific community. Ukrainian legal definitions also create precedents for avoiding responsibility for violations of academic integrity. The conclusions note some positive innovations in Ukraine, including borrowing forms of accreditation examinations and the work of a special agency to ensure the quality of education.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".