Pengaruh Good University Governance dan Manajemen Risiko terhadap Kinerja Perguruan Tinggi Negeri di Indonesia
Bibliographic record
Abstract
Pencapaian kinerja PTN di Indonesia diukur berdasarkan Indikator Kinerja Utama (IKU) yang diatur didalam Keputusan Menteri Pendidikan dan Kebudayaan (Kepmendikbud) Nomor 3 Tahun 2021. Hasil evaluasi pada tahun 2020 menunjukkan bahwa sebagian besar kinerja PTN masih belum menunjukan hasil yang yang diharapkan. Penerapan model GUG dan Manajemen Risiko pada PTN belum banyak tersedia pada literatur yang ada saat ini. Tujuan penelitian ini mencoba mengkaji hubungan penerapan GUG dan Manajemen Risiko terhadap kinerja PTN di Indonesia. Metode yang digunakan dalam penelitian ini untuk mencapai tujuan diatas adalah Structural Equation Model yang berbasis varian yaitu Partial Least-Squares (SEM-PLS). Hasil dari analisis menunjukkan bahwa penerapan GUG yang baik akan meningkatkan kualitas kurikulum dan pembelajaran dan juga meningkatkan kualitas tata kelola PTN. selain itu, penerapan Risk Management yang baik memiliki pengaruh positif dalam meningkatkan kualitas lulusan pendidikan tinggi.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".