TINJAUAN PEMANFAATAN DANA BAGI HASIL (DBH) SAWIT YANG DISALURKAN DI KABUPATEN TOLITOLI
Bibliographic record
Abstract
Dana Bagi Hasil (DBH) Perkebunan Sawit adalah DBH yang dialokasikan berdasarkan persentase atas pendapatan dari bea keluar dan pungutan ekspor atas kelapa sawit, minyak kelapa sawit mentah dan produk turunannya. DBH sawit merupakan Transfer ke Daerah. Pemerintah telah memprioritaskan penggunaan DBH Sawit berdasarkan Peraturan Menteri Keuangan Republik Indonesia, Nomor : 91 Tahun 2023 yakni pembagian persentase 80% diperuntukkan untuk pembangunan dan pemeliharaan Infrastruktur jalan, 20% penggunaan untuk kegiatan lainnya. Tulisan ini bermaksud untuk mengetahui bagaimana pemanfaatan DBH sawit yang disalurkan di Kabupaten Tolitoli, menggunakan metode kajian dokumen dan metode observasi partisipatif dengan teknik penyajian bersifat deskriptif berdasarkan data sekunder. Dari hasil pembahasan dan analisis pada kajian ini ditemukan kesimpulan bahwa pemanfaatan DBH sawit yang disalurkan di Kabupaten
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".