Indonesian Universities: Rapid growth, major challenges
Bibliographic record
Abstract
As befits its size and rising income, Indonesia now has one of the largest and fastest-growing tertiary education systems in the world. In 2010, about 5.2 million students were enrolled in some sort of institute of higher education, including universities, academies, polytechnics and advanced schools (sekolah tinggi), with almost three times as many enrolled in private as in public institutions. These students were enrolled in about 3,600 institutions administered mainly by the Ministry of Education and Culture and the Ministry of Religious Affairs. he focus of this chapter is on the country's approximately 550 universities, which attract most of the public funding, and which are seen as the major vehicle for lifting the standard of knowhow and intellectual discourse, and for providing high-level policy advice to government. We commence by making five broad generalizations about these institutions. First, Indonesian universities are essentially a creation of the second half of the twentieth century, with most of the growth occurring in the last quarter of that century. For all practical purposes, Indonesia barely possessed a tertiary education sector in the colonial era; during the first two decades of independence the growth of the sector was constrained by other nation-building priorities, including the necessity to expand primary and secondary education, and by the country's indifferent economic performance. Second, as a result of this history, the country has been an educational laggard, consistently ranking behind the Asian giants, China and India, and behind its middle-income ASEAN neighbours. Educational disadvantage typically takes decades to overcome, even with very high levels of expenditure and commitment, neither of which Indonesia has in abundant proportions. A third feature is that the sector began to grow very rapidly from the 1980s, driven by several factors. One was the large cohort beginning to graduate from the country's primary and secondary schools as a result of the commitment to universal education at these levels. Another was that the country was by then about to graduate into the ranks of lower middle-income developing countries, crossing a threshold where the demand for higher education would become highly income-elastic, and the labour market more �credentialed� in the sense of requiring more formal professional qualifications, and demanding a more skilled workforce. Moreover, the private tertiary education sector began to grow quickly, and was by then operating in a somewhat less restrictive regulatory regime.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".