PENGUKURAN INDIKATOR PROGRAM PEMBANGUNAN BIDANG SOSIAL KOTA MALANG TAHUN 2022
Bibliographic record
Abstract
Pengukuran Indikator Pembangunan Bidang Sosial Kota Malang dilakukan untuk dapat mengukur capaian indikator kinerja program bidang sosial pada tahun 2021 dan tahun 2022 hingga triwulan dua, review target indikator pembangunan bidang sosial yang telah ditetapkan, menyajikan data-data indikator sosial, dan dapat memberikan rekomendasi kebijakan serta langkah-langkah yang perlu dilakukan oleh Pemerintah Kota Malang berdasarkan hasil kajian. Terdapat 23 (dua puluh tiga) program P-RPJMD dan 54 (lima puluh empat) indikator pada bidang sosial yang sesuai dengan perangkat daerah terkait. Pengukuran indikator program pembangunan bidang sosial ini memiliki teknik analisis pengukuran capaian indikator bidang sosial, pengukuran efektivitas capaian target, pengelompokan tingkat efektivitas capaian target, dan identifikasi faktor determinan pelaksanaan program. Hasil dari analisis tersebut menunjukkan bahwa sejumlah 80% indikator telah memiliki tingkat efektivitas yang sangat tinggi di tahun 2021. Efektivitas tersebut meningkat dari tahun sebelumnya dengan selisih 23%. Akan tetapi masih terdapat 3 (tiga) indikator yang memiliki tingkat efektivitas sangat rendah dan 2 (dua) indikator memiliki tingkat efektivitas rendah.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 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".