Strategi Manajemen Perpustakaan MAN Asahan dalam Meraih Peringkat Terbaik I Tingkat SLTA Se-Sumatera Utara Tahun 2021
Bibliographic record
Abstract
A good library that is able to provide the right information for users,it’s all based on good management as well. The function of managements is so that goals can run in an orderly manner as expected. In this study using field research with a qualitative approach. Techniques used for data collection using observation, interviews and documentation. Meanwhile, the data analysis technique went throught the stages of data reduction, data presentation and verification. In this study, researchers want to examine the MAN Asahan library in management functions, namely planning, organization, actuacting, controlling and evaluation. The result of this study are that the MAN Asahan library has three planning programs, namely long-term, medium-term and short-term programs. Which the task is carried out or carried out in accordance with the duties of the libraries who work in accordance with their respective fields. The shortage of the MAN Asahan library is still the lack of the number of collection and the facilities of the MAN Asahan library are still many that are not suitable for users. Keywords: Library Management, Management Function
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".