Bibliographic record
Abstract
The current study discusses the different applications of higher education financing systems and analyze the contribution of different actors participating in higher education financing.The main objective of the study is to determine the share of participation of the state, private sector and other entities in financing higher education in different countries of the world. During the research, the author used general scientific methods and techniques, like analysis, synthesis, comparison, general-ization with the corresponding conclusions.According to the results of the study, the participants in higher education financing in every country are different from each other. Some countries have distinct finance systems in higher education. In some counties as the USA, the United Kingdom and Korea the participation of private sector is more impor-tant than public sector’s participation, in most European countries public sector is more dominant. Most countries spend more than an average of 1.5% of GDP on higher education financing, this rate exceeds 2.3% of GDP in some countries such as Canada, Korea and the USA but some other countries such as Belgium, Italy and the Germany allocate less than 1.5% of GDP. Most OECD members support higher education and its actors by using public funds which is more or less 22% of their public budgets. The practical significance of the study is that the results of the research can be applied in the prepara-tion of the analytical part of programs for the development of higher education in Kazakhstan
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.023 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".