European Values in the Ukrainian Higher Education System: Adaptation and Implementation
Bibliographic record
Abstract
The study of key European values in the Ukrainian higher education system based on an analysis of certain aspects of their implementation and adaptation. The participants of this study are 125 teachers from different higher education institutions of Ukraine. Instruments: a questionnaire was developed and distributed via social media and e-mail, and interviews were conducted. Quantitative and qualitative analysis was used, the answers of the respondents were also processed using statistics, the average indicators and possible deviations were determined when analysing the material received. The importance of the processes of implementation and adaptation of European values in the higher education system of Ukraine is proved. The results of the study show that the vast majority of participants consider European values such as democracy, human rights, trust, tolerance, and equality important for integration into the Ukrainian higher education system. More than 50% of respondents believe that the current level of integration of European values into the curricula and pedagogical activities of higher education institutions in Ukraine is effective. However, this process carries challenges and tasks that require attention and additional efforts for further improvement. Conclusions: The implementation of European values in the Ukrainian higher education system is a complex and multifaceted process that requires careful development and gradual implementation. It is determined that the possible obstacles to the process of full adaptation of European values in higher education in Ukraine are both objective (lack of resources, bureaucratic obstacles, etc.) and subjective (socio-cultural differences and resistance of staff, low level of motivation).
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".