Inovasi Sistem Layanan dan Rujukan Terpadu (SLRT) Bersahaja di Kabupaten Pringsewu: Evaluasi dan Kontribusinya terhadap Pengentasan Kemiskinan
Bibliographic record
Abstract
SLRT Bersahaja merupakan inovasi sistem layanan terpadu di Kabupaten Pringsewu yang bertujuan untuk meningkatkan akses masyarakat miskin terhadap program perlindungan sosial dan pengentasan kemiskinan. Penelitian ini bertujuan untuk mengevaluasi efektivitas SLRT Bersahaja dalam mendukung upaya pengentasan kemiskinan, mengidentifikasi peran Tim Reaksi Cepat (TRC), serta menganalisis validasi data penerima manfaat. Metode penelitian menggunakan pendekatan campuran dengan pengumpulan data primer melalui wawancara, survei, dan observasi, serta data sekunder berupa dokumen resmi. Hasil penelitian menunjukkan bahwa 87% penerima manfaat merasa layanan mudah diakses, 82% menyatakan bantuan sesuai kebutuhan, dan 75% puas dengan kecepatan respons TRC. Selama observasi, TRC menangani 25 kasus mendesak dengan waktu respons rata-rata 2-3 jam. Validasi data penerima manfaat mencapai 92% akurasi, menunjukkan efektivitas sistem dalam menyalurkan bantuan tepat sasaran. Namun, kendala berupa keterbatasan sumber daya manusia dan akses teknologi di daerah terpencil masih menjadi tantangan. Kesimpulannya, SLRT Bersahaja berhasil meningkatkan kesejahteraan masyarakat miskin di Kabupaten Pringsewu dan relevan dengan tujuan pembangunan berkelanjutan. Rekomendasi penelitian selanjutnya mencakup penguatan kapasitas sumber daya manusia dan pengembangan infrastruktur digital untuk meningkatkan cakupan layanan.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".