World-Class University Market: Rethinking Geopolitical and National Stereotypes
Bibliographic record
Abstract
The article considers the results of the third wave of identification of worldclass universities in 2021, obtained on the basis of the authors' methodology. The comparison of the new results with the data for the 2017 and 2019 allowed us to examine in more detail some well-established mental stereotypes of a geopolitical and national character. In particular, the role of the North American university center is declining, but the universities of the United States and Canada are still models for the rest of the world, both in terms of the breadth of scientific diversification and in terms of the research results obtained. The seemingly self-evident "decline of Europe" concerning the market of advanced universities is not confirmed. Moreover, there is reason to talk about the growing activity of the European geopolitical center, whose universities not only hold their positions but also rapidly increase the number of highly specialized institutions and are at the forefront of training personnel for post-industrial society. Contrary to many expectations, the Asian university market is still far from becoming a distinctive authentic phenomenon and is still only an example of a relatively successful "copying model" of Western models. Quite unexpected was the alarming conclusion about the superiority of advanced universities in Latin America over universities in the post-soviet space in general and in Russia in particular. It is shown that the recognition of "new" world-class universities by international rating agencies, such as the National Autonomous University of Mexico, is very late. The internal Russian mental archetype concerning the model of development of the Lomonosov Moscow State University is recognized as untenable, whose tenure as a member of world-class universities is extremely unstable.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".