Bibliographic record
Abstract
Purpose – discuss the different applications of higher education financing systems and analyze the contribution of different actors participating in higher education financing. Methodology – in this work the authors used the following scientific methods of research: analysis, synthesis, comparison, generalization. Originality / value – The first part of this study focuses on the theoretical framework of higher education services, and the second part provides a comparison of the shares of the actors who contribute to higher education financing in the countries in question. Thus, a comparative analysis of higher education system among countries is conducted. Findings – this study reveals the fact that the participants in higher education financing in every country are different from each other and that some countries have distinct finance systems in higher education. While the participation of private sector in the USA, the United Kingdom and Korea is more important than public sector’s participation, public sector is more dominant in most European countries. 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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".