IMPLEMENTASI CENTER OF SERVICE FOR RESEARCH (CoSfRe) DALAMMEWUJUDKAN PERENCANAAN BERBASIS DATA DI KABUPATEN MAGELANG
Bibliographic record
Abstract
Saat ini sebagian besar pemerintah daerah belum melaksanakan perencanaan pembangunan berbasis data. Padahal untuk bisa menyelenggarakan jasa publik secara optimal, diperlukan penerapan perencanaan berbasis data. Makalah ini dimaksudkan untuk mendeskripsikan sejauh mana Center of Service for Research (CoSfRe) berhasil dilaksanakan dan berkontribusi terhadap perencanaan pembangunan daerah berbasis data di Kabupaten Magelang. Penelitian menggunakan pendekatan kualitatif, teknik pengumpulan data dilakukan melalui studi dokumentasi dan wawancara serta menerapkan metode analisis data secara deskriptif kualitatif. Hasil penelitian ini menunjukkan bahwa implementasi CoSfRe telah menampakkan keberhasilan dalam meningkatkan pelayanan Bappelitbangda terhadap para peneliti dan menghasilkan rekomendasi sebagai input perencanaan pembangunan. Kemampuan inovator dalam meyakinkan stakeholder dan manfaat yang telah dirasakan oleh stakeholder menjadi kunci keberhasilan CoSfRe.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.083 | 0.022 |
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".