FOREIGN EXPERIENCE IN DIGITALIZATION OF PUBLIC MANAGEMENT OF HIGHER EDUCATION
Bibliographic record
Abstract
The article analyzes the current problems of the digitalization of public management of higher education. The mentioned relevance of the study is due to the need to increase the efficiency of public management of higher education by implementing modern digital technologies, adapting international experience and responding to the challenges of digital transformation of the educational space. The main goal is to analyze the foreign experience of digitalization of public management of higher education and determine the possibilities of its productive adaptation in Ukraine. The study aims to examine international practices of digitalization of public management of higher education, assessing their effectiveness and developing recommendations for applying best practices in the national education system. The work uses such methods as comparative analysis, content analysis of scientific sources and official documents, case studies of foreign practices of digitalization, as well as a systematic approach to assessing the effectiveness of management processes in higher education. The results of the study demonstrated that foreign practices of digitalization of public management of higher education increase the efficiency of management processes. The article defines concepts, models and key components in the context of higher education. The advantages and challenges of the digitalization of public management of higher education are identified. Foreign experience in digitalization of public management of higher education is studied, in particular, the practices of the EU, the USA, Canada, Japan, South Korea and China. The effectiveness of the use of digital technologies in public management of higher education is substantiated. An assessment of the state of digitalization of public management of higher education in Ukraine is given. The possibilities of adapting foreign experience to Ukrainian realities are identified. Recommendations are developed for adapting the best foreign practices of digitalization to increase the efficiency of public management of higher education in Ukraine. As a result, it was found that digitalization of public management of higher education significantly increases the efficiency of management processes, contributes to the integration of innovative practices and can be successfully adapted to the national education system. Future research should be aimed at developing effective models of digitalization of public management of higher education in Ukraine.
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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 | 0.002 | 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 | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".