Perlukah Tata Kelola Pengelolaan Dana Kemahasiswaan Dilakukan?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fenomena terjadinya normalisasi kecurangan dana kemahasiswaan di Lembaga Kemahasiswaan Fakultas menunjukkan terjadinya indikasi praktik kecurangan di Lembaga Kemahasiswaan. Penelitian ini bertujuan untuk mengetahui seberapa efisien penerapan Good University Governance (GUG) di Fakultas X, Universitas YZ dalam mencegah terjadinya kecurangan dana kemahasiswaan. Penelitian ini menggunakan metode deskriptif kualitatif dengan teknik pengumpulan data yang dilakukan dengan cara wawancara dan penyebaran kuisioner kepada badan legislatif fakultas, badan eksekutif fakultas, dan unit fakultas. Tahapan dalam penelitian ini terbagi dalam tiga bagian yaitu reduksi data, penyajian data, dan penarikan kesimpulan. Hasil penelitian menunjukkan prinsip Good Government University (GUG) yang telah dilakukan oleh Lembaga Kemahasiswaan di Fakultas X, Universitas YZ berjalan cukup baik, namun masih perlu ditingkatkan lagi untuk memaksimalkan pengelolaan dana kemahasiswaan. Diharapkan penelitian ini membawa manfaat bagi Lembaga Kemahasiswaan Fakultas X, Universitas YZ dalam menilai risiko kecurangan dan pengendalian yang diperlukan di dalam pengelolaan dana kemahasiswaan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 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 it