Means of Forming a Culture of Academic Integrity of Postgraduate Students: Experience of Ukraine and the European Union
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 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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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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