Analisis Perencanaan Keuangan APBD Pada Badan Keuangan Kabupaten Kepulauan Sangihe Tahun Anggaran 2018
Bibliographic record
Abstract
Tujuan penelitian ini untuk mengetahui proses keuangan APBD, dan mengetahui faktor-faktor yang menghambat perencanaan keuangan, Untuk mengetahui upaya-upaya dalam melakukan penanganan perencanaan pada Badan Keuangan Kabupaten Kepulauan Sangihe, Penelitian ini menggunakan pendekatan deskriptif kualitatif dengan pengumpulan data observasi, wawancara dan dokumentasi, informasi dihimpun secara langsung melalui wawancara pada kepala dan pegawai badan keuangan kabupaten kepulauan sangihe, Teknik analisis data yang digunakan yaitu analisis domain, taksonomi, komponensial dan tema kultural, Hasil penelitian ini menunjukan bahwa perencanaan Anggaran Pendapatan dan Belanja Daerah (APBD) dan pemerintah daerah, pemerintah pusat dalam hal ini Kementerian Dalam Negeri mengeluarkan peraturan Kementerian Dalam Negeri setiap tahun. nomor 13 tahun 2006 Tentang pedoman penyusunan apbd agar menjadi pedoman pemerintah daerah.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".