MANAJEMEN PUSAT KESEJAHTERAAN SOSIAL DESA SEKARWANGI KECAMATAN SOREANG KABUPATEN BANDUNG
Bibliographic record
Abstract
Pusat Kesejahteraan Sosial (Puskesos) merupakan organisasi berbasis masyarakat yang memiliki peran dalam penanganan kemiskinan di tingkat desa/kelurahan. Nilai strategis Puskesos yaitu adanya Basis Data Terpadu (BDT) yang memuat data warga miskin dan rentan miskin di desa/kelurahan. Puskesos juga merupakan organisasi yang menggunakan pendekatan single window services untuk penyaluran bantuan sosial. Optimalisasi fungsi Puskesos salah satunya dapat dilihat dari perspektif manajemen. Penelitian ini menjabarkan manajemen Puskesos Exen-Bersama serta penerapan Model ARC untuk peningkatan efektivitas manajemen Puskesos. Manajemen Puskesos dilihat dari tingkah laku manajerial dan komponen organisasi yang terdiri dari institutional subsystem, management subsystem, dan production subsystem. Penelitian aksi dan tindakan digunakan sebagai metode penelitian serta melibatkan penerapan teknik penelitian partisipatif. Intervensi yang telah dilakukan berdampak pada perubahan pada tiga komponen organisasi berupa pengukuhan Peraturan Desa tentang Puskesos, manajemen data warga Desa Sekarwangi, dan data warga yang dapat diakses cepat. Penelitian ini memberi gambaran bahwa Puskesos sebagai organisasi pelayanan manusia dapat berperan lebih efektif dengan melakukan pembenahan aspek-aspek manajemen. Kata Kunci: Puskesos; Manajemen; single window services; Model ARC
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".