FINANCIAL POLICY OF THE HIGHER EDUCATION INDUSTRY: FOREIGN EXPERIENCE FOR UKRAINE
Bibliographic record
Abstract
The article describes the mechanism of financing the development of the higher education system. Several models of education financing, flows and sources of financial support for higher education institutions are considered. The financial policy of higher education in different countries of the world is analyzed: Canada, Great Britain, France, Japan, Sweden, Norway, China, Nigeria, Brazil, Argentina, India, Greece, Italy, Denmark, Finland, Israel, the Netherlands and the USA. It is noted that in Ukraine some elements of the American system of tax benefits for educational services could be applied. Diversification of sources of education funding is one of the ways to reduce the resource dependence of free economic education on the state. Examining the state of financing of higher education and sources of financial resources, it is indicated that it is necessary to include in the priority areas of improving the system of financing education in the context of ensuring the competitiveness of free economic education.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".