Perlukah Tata Kelola Pengelolaan Dana Kemahasiswaan Dilakukan?
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.008 |
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".