PERENCANAAN KINERJA YANG BERKUALITAS SEBAGAI BAGIAN DARI PENINGKATAN KUALITAS SISTEM AKUNTABILITAS KINERJA INSTANSI PEMERINTAH (SAKIP) KABUPATEN PENUKAL ABAB LEMATANG ILIR Pada Tahap I RPJPD 2025-2045 Melalui Pelaksanaan RPJMD 2025-2029
Bibliographic record
Abstract
Policy Paper ini mengusung tema: Menuju Perencanaan Kinerja Berkualitas sebagai Bagian dari Sistem Akuntabilitas Kinerja Instansi Pemerintah Kabupaten Penukal Abab Lematang Ilir (PALI) yang Berkualitas. Terdapat tiga permasalahan yang dihadapi dalam peningkatan kualitas perencanaan kinerja, yaitu: Bagaimana menjawab isu strategis melalui penetapan tujuan dan sasaran secara Tepat, Penentuan Indikator kinerja yang belum sepenuhnya sesuai dengan kaidah SMART (Spesific, Measurable, Achievable, Relevance, Timebound); dan ketidakselarasan antar Dokumen Perencanaan Kinerja. Makalah Kebijakan ini merekomendasikan 4 (empat) Kebijakan yan ditujukan kepada pihak-pihak terkait dalam rangka menyelesaikan permasalahan di atas, yaitu antara lain: Menyusun Pedoman Casecading Kinerja sebelum dituangkan ke dalam Dokumen Perencanaan; Pentingnya Perencanaan dan Penganggaran Berbasis Kinerja; Bagaimana menentukan dan menetapkan indikator kinerja secara tepat, serta bagaimana meningkatkan kapasitas Sumber Daya Manusia.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.124 | 0.033 |
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".