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Record W4402063375 · doi:10.58411/htgydn51

PENGUKURAN INDIKATOR PROGRAM PEMBANGUNAN BIDANG SOSIAL KOTA MALANG TAHUN 2021

2022· article· id· W4402063375 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePANGRIPTA · 2022
Typearticle
Languageid
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.736
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.003

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.

Opus teacher head0.022
GPT teacher head0.215
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it