IMPLEMENTASI PENYUSUNAN RENCANA KERJA (RKP) DESA (Studi Di Desa Trucuk Kecamatan Trucuk Kabupaten Bojonegoro)
Bibliographic record
Abstract
Dalam penyusunan dokumen Rencana Kerja Pemerintah (RKP) Desa harus sesuai dengan Peraturan Menteri Dalam Negeri Republik Indonesia Nomor 114 Tahun 2014 Tentang Pedoman Pembangunan Desa, akan tetapi selama ini proses penyusunan dokumen RKP Desa Trucuk Kecamatan Trucuk Kabupaten Bojonegoro belum sesuai dengan Permendagri tersebut. Penelitian ini bertujuan untuk menjelaskan proses implementasi penyusunan RKP Desa, metode yang digunakan ialah pendekatan kualitatif dengan pengambilan data observasi, wawancara dan dokumentasi. Penentuan informan menggunakan purposive snowball sampling. Analisis data menggunakan Model Spradley yang dimulai dari analisis domain, analisis taksonomi, analisis komponensial, dan analisis tema kultural. Hasil penelitian menunjukkan bahwa Implementasi Penyusunan RKP Desa Trucuk belum sesuai dengan Peraturan Menteri Dalam Negeri Republik Indonesia Nomor 114 Tahun 2014 Tentang Pedoman Pembangunan Desa, hal ini terjadi karena penyusunan RKP Desa Trucuk masih mengacu pada Panduan Teknis Musyawarah Perencanaan Pembangunan Kabupaten Bojonegoro tahun 2017 yang belum diselasarkan dengan Peraturan Menteri Dalam Negeri Republik Indonesia Nomor 114 Tahun 2014 Tentang Pedoman Pembangunan Desa. Kata kunci : Implementasi, Rencana Kerja Pemerintah (RKP) Desa
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".