A Study on the Development Plans and Financial Investment Strategies for Higher Education in Major OECD Countries
Bibliographic record
Abstract
This study aims to explore implications for the development and investment direction of domestic higher education by examining the medium- to long-term development plans and financial investment strategies of higher education in major OECD countries. To this end, we analyzed domestic and international statistical data, including those from the OECD, and examined the medium- to long-term development plans and investment strategies of higher education in six countries – the United States, Japan, Australia, Canada, France, and Finland – based on government publications. The major research findings are as follows. First, major countries commonly regard education, research, and community engagement as core functions of universities and establish medium- to long-term development plans accordingly. Second, the goals and strategies of these higher education development plans are established around key values such as autonomy, excellence, inclusiveness, and equity, and are accompanied by corresponding financial investment plans. Finally, based on the examination of the medium- to long-term development plans for higher education in these major countries, we propose directions for the development and investment of domestic higher education, focusing on the expansion of financial resources, the evolving functions and roles of universities, and the higher education attainment system.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".