PENGUKURAN INDIKATOR KINERJA DAERAH KOTA MALANG TAHUN 2021
Bibliographic record
Abstract
Indikator Kinerja adalah tanda yang berfungsi sebagai alat ukur pencapaian kinerja suatu kegiatan, program atau sasaran dan tujuan dalam bentuk output, outcome, impact. Indikator Kinerja Daerah meliputi Indeks Pembangunan Manusia (IPM), indeks pendidikan, indeks kesehatan, indeks daya beli, angka kemiskinan, persentase penurunan PMKS (Penyandang Masalah Kesejahteraan Sosial), indeks pembangunan gender, IndeksPembangunan Masyarakat (IPMas), dan indeks modal sosial. Tujuan Penyusunan Indikator Kinerja Daerah Kota Malang tahun 2021 yaitu untuk mengukur capaian indikator kinerja daerah tahun 2021, mengukur capaian indeks pembangunan masyarakat dan indeks modal sosial tahun 2021 berdasarkan data primer yang dilakukan secara survei, melakukan review terhadap target/sasaran indikator-indikator tersebut yang telah ditetapkan di Rancangan Peraturan Daerah Perubahan RPJMD Kota Malang tahun 2018-2023, melakukan analisis antara hasil capaian yang diperoleh pada tahun 2021 dengan target/sasaran yang telah ditetapkan di Rancangan Peraturan Daerah Perubahan RPJMD Kota Malang tahun 2018-2023, memberikan rekomendasi kebijakan dan langkah-langkah apa yang perlu dilakukan oleh Pemerintah Kota Malang berdasarkan hasil penelitianberdasarkan hasil penelitian. Metode analisis yang digunakan dalam penelitian ini yaitu Analisis Faktor Pendekatan Principal Component Analysis (PCA), dan Gap Analysis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 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".