Integration and differentiation of leadership higher education: lessons for Ukraine from the EU, USA and China
Bibliographic record
Abstract
The article is devoted to the analysis of the processes of integration and differentiation of leadership higher education in the world's leading state associations of various degrees of unitarity - the EU, the USA, China. In 2023, they concentrated 69% of world-class universities (WCU), ranked 1-500 in the ShanghaiRanking (ShR), its general version (ARWU), compared to 67% in 2004. During this time of unchanged rating methodology, the number of WCU in the EU decreased by 15%, in the USA by 31%, on the other hand, in China it increased by 7.3 times. At the same time, averaged by country/state(district)/region(province, etc.), the best university place in the ranking in the EU deteriorated by 9%, in the USA by 44%, while in China it improved by 2.8 times. At the same time, the 1st university place for the USA remains stable, the EU in the comparative composition of countries (without the United Kingdom) moved from 5th to 3rd position (thanks to France), China – from 25th to 5th place, bypassing Japan and Canada within the group of extra-class universities (ECU), which are on 1-30 places of the extremely steep section of the rating. In terms of the level of university achievements, between the USA and the EU and between the EU and China, there are two European countries that are not members of the European Union: The United Kingdom (2nd position) and Switzerland (4th place), respectively. Examining the dynamics of university achievements of individual countries, states (district), regions (provinces, etc.) in the EU, the USA, and China, respectively, shows that the integration of universities into the single spaces of higher education in the EU, the USA, and China does not protect against university differentiation as a result of competition. Success is observed in those countries/states(district)/regions (provinces, etc.) that independently or together with the central union/federal/national government purposefully support the formation and development of WCU. Own institutional efforts are not enough. In this regard, in the period 2004-2023, no such universities were created in 10 EU countries, instead, Hungary lost all 3 that it had. In the same way, 2 states of the USA have not created USC, and 11 states have lost those that they had. In China, on the contrary, thanks to the strong and consistent state policy regarding the development of USCs, for which ShR was created, WCUs have appeared in 19 regions, in 3 - their number has increased many times, and only in 7 - there are none yet such institutions. It was concluded that the development of WCU requires, in addition to endo-institutional, also exo-institutional targeted measures. Taking into account the experience of the EU, the USA, and China will contribute to the formation of leading universities in Ukraine, which is important for the approaching victory in the war, the post-war recovery of the country in terms of its entry into the European Union. At the same time, the findings of the study characterize the state of implementation of the second dimension of the European area of higher education regarding competitiveness and attractiveness.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".