Globalization in the field of higher education in focus of macro-analysis: trends and problems
Bibliographic record
Abstract
The article deals with globalization of higher education. Higher education is grouped around many of the key issues of globalization: the internationalization strategy; transnational education; providing international quality; entrepreneurial approaches for education; regional and interregional cooperation; information and communication technologies and virtual schools; the emergence of new educational mediators – education providers, the problems of equality and access to education and so on. More of globalization produce new relationships of exchange, the internationalization of trade, restructuring of the international labor market, reduce labor conflicts at the level of capital, international division of labor, the development of new forces of production and technology, capitalintensive production, increasing the number of women employed in industrial and economic processes, increasing the size and value of services. It should be noted that the higher education system is able to influence globalization, forming a line of future policy, and region. It is reported that leaders of the globalization process in general and in particular the integration processes and the processes of formation of the education market internationally are leading countries that embarked on the path of transformation of their education systems and consider an active part in shaping the world educational space as a factor in solving the existing problems national and international levels. These countries are the United States, Canada, Western Europe, Australia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".