Pengadaan Koleksi Muatan Lokal “Local Content ”sebagai Upaya Pelestarian Budaya Daerah di Dinas Kearsipan dan Perpustakaan Provinsi Sumatera Barat
Bibliographic record
Abstract
AbstractThe writing of this paper aims to describe: (1) local content collection services, (2) constraints in procuring local content collections; and (3) efforts in procuring local content collections in the Archives Service and West Sumatra Province Library. The method of writing this paper is to use a qualitative descriptive method which is done by observation, interviews and data collection directly to the source, the Office of Archives and the Library of West Sumatra Province. Based on the results of interviews and observations conducted at the Archives Service and West Sumatra Province Library are as follows: First, the service collection of "local content" content at the Archives Service and West Sumatra Province Library through the procurement of local content collection, local content collection service system and the use of collections local content by users in the Archives Service and Library of West Sumatra Province. Second, the obstacles faced are: (1) limited number of local content collections, (2) limited access to local content collections, (3) lack of government and other related parties' attention to cultural functions, (4) lack of human resources as skilled librarians. Third, the efforts that have been made by the Office of Archives and Library of West Sumatra Province, namely conducting education, outreach, law enforcement and infrastructure facilities for local content collection works in an effort to preserve regional culture.Keyword: local content collection; cultural preservation; public library
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".