Formulating Social Security Policy Models for Higher Education: A Funding Transformation for Inclusive and Sustainable Higher Education Access
Bibliographic record
Abstract
This study aims to formulate social security policy models for higher education financing to transform access into a more inclusive and sustainable system.Employing an integrative literature review method, this research critically analyzes policies and synthesizes findings from scholarly articles, government documents, and international reports.Inclusion criteria focused on studies addressing higher education funding mechanisms, social security integration, and comparative international financing models.The results indicate that while Indonesia has established an Education Endowment Fund valued at IDR 156 trillion, its current utilization is limited to 7-8%, covering only about 1 million students per year, leaving a significant accessibility gap for approximately 3 million potential students.The study reveals persistent challenges in equity, governance transparency, and fiscal sustainability.To address these gaps, the research proposes a comprehensive framework comprising three models: 1) an equitable revolving fund model using income-contingent student loans to ensure sustainable funding and minimize fiscal dependency; 2) a high-talented person scholarship model to strategically invest in exceptional individuals and prevent brain drain; and 3) an Educational Savings Model integrated with social security contributions (BPJS), empowering families to systematically prepare for higher education costs.Quantitatively, these models are projected to increase Indonesia's Gross Participation Rate (GPR) in higher education by at least 4% annually, surpassing the current growth rate of only 2% per year.The findings emphasize that transitioning to a social security-based higher education financing ecosystem enhances intergenerational equity, reduces long-term fiscal risk, and fosters national competitiveness.
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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 | 0.021 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".