Indonesian Universities: Rapid growth, major challenges
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,016 | 0,014 |
| Science ouverte | 0,002 | 0,008 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,006 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».