Export of Russian education in the mechanism of “Soft Power”
Bibliographic record
Abstract
The article analyzes Russia’s strategy for creating a positive image of the state, attracting foreign citizens to Russian universities. As part of the study, a survey of citizens from Kazakhstan, China, Uzbekistan studying at universities of the Russian Federation was carried out. It was found that the lower the quality of life in the respondent’s country, the more he has a desire to stay working in Russia, and the higher the material well-being of the respondent, the more he wants to continue studying and working abroad (USA, Canada, etc.). It is revealed that the higher the level of education of students’ parents and the position they hold, that is, the more well-off a student’s family is, the more often he focuses not only on university ratings, but also on quality of life ratings, happiness index, security, investments in human capital, etc. The authors concluded that the majority of foreign citizens tend to enroll to Russian universities on the recommendation of their reference personalities (usually parents, grandparents), who at one time received higher education in the Russian Federation. The results of the study showed that in order for Russia to enter the top five countries where foreign students study the most, it is necessary for the state and business to use the resources of “soft power”, which the author points out in his article.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".