Internationalization of higher education as a factor in the competitiveness of a technical university
Bibliographic record
Abstract
Internationalization serves the purpose of increasing the world rating of an educational institution, contributes to improving the quality of education in general and is one of the main factors in the competitiveness of a technical university in the modern world. The article describes the main directions of the development of this process in the frame of Nazarbayev University experience, the Canadian and Russian experience of research to establish a framework of capacity building for internationalization. This study is carried out during the implementation of the project "Capacity building for the internationalization of a technical university by means of digital learning technologies" (IRN project АP08052214), approved by the priority "Scientific foundations" Mangilik el "(education of the XXI century, fundamental and applied research in the humanities)" grant funding for young scientists for 2020-2022 by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan. The author considers different approaches in understanding the process of internationalization, investigates the methodology of benchmarking, the process of internationalization of the university, presents the goal, scientific methods and potential of the project. The primary analysis of the studied works on the internationalization of higher education showed that there were problems in the results of international activities of national and foreign practices and the lack of work on capacity building for the internationalization of technical universities.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".