Pengelolaan tata arsip di dinas kearsipan dan perpustakaan daerah Kabupaten Karanganyar
Bibliographic record
Abstract
<p dir="ltr"><span>This study aims to determine: (1) The implementation of archive management at the Regional Archives and Libraries Office of Karanganyar Regency. (2) Obstacles faced in archive management at Regional Archives and Libraries Office of Karanganyar Regency. (3) What efforts will be made to overcome obstacles encountered in records management in the Regional Archives and Libraries Office of Karanganyar Regency? This research is qualitative research with a case study approach. The data sources used in this study are informants, places and events, and documentation by using purposive sampling technique. Techniques for data collection of interviews are observation and analysis of documents. They are testing data validity in the form of source and method triangulation. Then, interactive analysis model data analysis techniques are used. The results of the research: (1) The implementation of archive management at the Regional Archives and Libraries Office of Karanganyar Regency includes receiving, recording, storing, maintaining, shrinking, and destructing archives. (2) The obstacles faced in archive management at the Regional Archives and Libraries Office of Karanganyar Regency are limited facilities and infrastructure to support archival activities and the lack of human resource</span></p><div><span><br /></span></div>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 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".