PENGUKURAN INDIKATOR PROGRAM PEMBANGUNAN BIDANG SOSIAL KOTA MALANG TAHUN 2021
Bibliographic record
Abstract
Pembangunan kesejahteraan sosial merupakan salah satu upaya mewujudkan keberhasilan di bidang sosial dan budaya. Kota Malang mengalami jumlah peningkatan Penyandang Masalah Kesejahteraan Sosial (PMKS) dan fakir miskin akibat dampak dari adanya wabah Covid-19 ini pada tahun 2021 ini. Pergeseran anggaran ke bidang kesehatan juga berdampak secara signifikan terhadap pencapaian indikator pembangunan bidang sosial. Tujuan dari penelitian ini adalah mengukur capaian indikator program pembangunan bidang sosial tahun serta mereview target sasaran indikator pembangunan sosial di Kota Malang. Analisis yang dapat digunakan untuk mengukur idnikator program pembangunan bidang sosial, yaitu angka kemiskinan, Indeks Modal Sosial (IMS), persentase penurunan PMKS, Indeks Pembangunan Masyarakat (IPMas), Indeks Pembangunan Gender (IPG), dan metode evaluasi target capaian urusan kegiatan bidang sosial. Berdasarkan hasil analisis diketahui bahwa angka kemiskinan di Kota Malang pada tahun 2016-2019 mengalami penurunan, nilai IMS Kota Malang terus mengalami peningkatan setiap tahunnya, persentase PMKS di Kota Malang pada tahun 2016-2021 mengalami penurunan, nilai IPMas mengalami kenaikan pada tahun 2021 sebesar 2,2% serta Kota Malang memiliki nilai IGP tinggi.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".