Penerapan SLiMS Pada Layanan Sirkulasi di Perpustakaan INSTIDLA
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
Tujuan dilakukannya penelitian ini adalah: (1) untuk mengetahui penerapan SLiMS pada layanan sirkulasi di perpustakaan Institut Teknologi dan Bisnis Diniyyah Lampung (INSTIDLA) telah berjalan secara optimal; (2) untuk mengetahui adakah masalah dalam penerapan SLiMS pada layanan sirkulasi di INSTIDLA. Penelitian ini termasuk kedalam metode analisis deskriptif dengan pendekatan kualitatif. Hasil penelitian ini adalah dengan penerapan SLiMS sangat membantu sekali dalam layanan perpustakaan khususnya layanan sirkulasi. Pustakawan mempunyai keahlian dalam penggunaan SLiMS dengan nilai “Baik” sehingga pemustaka merasa puas. Serta kualitas layanan yang diberikan perpustakaan dalam indikator “Baik”.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it