STRATEGI DAN RENCANA AKSI PENGEMBANGAN PELAYANAN SOSIAL PERKOTAAN (URBAN SOCIAL SERVICES) DALAM RANGKA MENINGKATKAN DAYA DUKUNG KAWASAN DI WILAYAH DKI JAKARTA.
Bibliographic record
Abstract
Artikel ini ditulis berdasarkan penelitian yang dilakukan di wilayah Propinsi Daerah Khusus Ibukota Jakarta bertujuan untuk meningkatkan dan mengembangkan fasilitas pelayanan sosial perkotaan sesuai dengan dinamika sosial dan ekonomi masyarakat dalam rangka mewujudkan pemerataan pelayanan sosial perkotaan yang adil sesuai dengan tingkatan kelompok masyarakat. Metode yang digunakan adalah metode survei dengan basis analisis data sekunder yang didukung oleh data hasil observasi lapangan serta perbandingan secara antar waktu (time series).Teknik analisis menggunakan statistik deskriptif kualitatif dan kuantitatif. Hasil penelitian menunjukkan bahwa ada tiga strategi yang dapat digunakan yaitu strategi kemitraan, strategi pemberdayaan dan strategi penguatan kelembagaan, sedangkan rencana aksi pengembangannya dirumuskan kedalam program kemitraan antara pemerintah pusat dan daerah, pemda dengan swasta, pemda dengan masyarakat; progam kemitraan tersebut juga dapat dilaksanakan melalui mekanisme fasilitasi pemda terhadap masyarakat dan swasta; program pemberdayaan dan program penguatan kelembagaan dan sumberdaya manusia dengan rencana aksi meliputi pengkajian kebijakan dan penyusunan rencana pelayanan sosial, Pengembangan model pelayanan sosial, pembentukan dan pengembangan sistem informasi, basis data serta jaringan kerja Kata Kunci: strategi, rencana aksi dan pelayanan sosial perkotaan
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.011 |
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".