Ranking Support for the Strategic Development of Leading Universities: Lessons from the University Revolution in China
Bibliographic record
Abstract
The article uses the example of China to justify that the use of ranking tools contributes to the effective strategic development of leading universities. Rating diagnostics of the effectiveness of relevant strategic measures makes it possible to identify and demonstrate targeted achievements. To characterize the unprecedentedly rapid and sustainable progress of China's top universities, a comparison is made with the avant-garde higher education of the United States, the world's university leader. The study used data from the objective, specially created in China, Shanghai Ranking for the period of unchanged methodology of its general version (ARWU) during 2004-2025, as well as the sectoral version (GRAS) 2017-2024. Thanks to the ranking assessment, it was found that university development in China occurs simultaneously in qualitative and quantitative dimensions. In terms of the number of universities in ARWU, mainland China (including universities in the special zones of Hong Kong and Macau) will surpass the United States starting in 2022. In terms of the number of world-class universities (in the top 500 group), in 2025 China (108) will be close to the US (111), although in 2004 it was 13 times behind. Over the past six years, China has expanded by 4 institutions into the group of extra-class universities (the top 30 group), which is in an extremely steep section of the ranking, displacing the only university in Japan from this cohort and overtaking the university in Switzerland. Thus, in terms of the best university achievement (18th place), China has moved from 25th position in the list of countries in 2004 to 4th now (more than a 6-fold improvement), trailing in the top 30 group only the USA (19 institutions), the United Kingdom (also 4 institutions) and France (1 institution) and ahead of the aforementioned Switzerland (1 institution) and Canada (1 institution). In terms of the sectoral version, there was an overall 2.5-fold improvement in university excellence/competitiveness over the seven-year period, with Chinese universities not deteriorating in any of the 55 academic subjects. In this version, the number of first places increased from 8 to 20 (36%), or 2.5 times. The ranking control confirmed the feasibility of creating and implementing programs of consistent state support in China for leading universities, especially the leading C9 League consisting of nine advanced institutions, four of which have now acquired extra-class status. Also, starting in 2015, a new strategic program has been implemented to create so-called dual world-class universities for the period until 2050, which provides for general and sectoral ranking monitoring and currently includes 147 selected institutions, which account for 4.9% of the total number of Chinese universities. The lessons from China for Ukraine primarily consist of: 1) the urgent introduction of a strategy for the development of leading universities with mandatory objective ranking of top universities into domestic policy and practice; 2) the allocation of a leadership group from among the best institutions and the provision of powerful and prolonged state support to them for the purpose of their synergistic group breakthrough to the heights of excellence (real provision of the priority of the strategic development of leading universities); 3) the expediency of strengthening trust in leading universities on the basis of trust in a reliable ranking and, on this basis, significantly expanding their institutional autonomy and resource provision as promising objects of state investment; 4) the implementation of a ranking-based strategy for the consolidation of the university network. This is important for increasing the competitiveness of the Ukrainian economy, strengthening defense capabilities and security, and the post-war reconstruction of the country on a highly professional and high-tech basis in a globalized competitive world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".