Bibliographic record
Abstract
Permasalahan yang di kemukakan dalam Penelitian ini adalah Implementasi Program KeluargaHarapan Di Kecamatan Sepauk Dengan ruang lingkup Penelitian, Mekanisme Penyaluran, Koordinasi,Laporan dan Pertangungjawaban, Sumber Daya Pelaksana PKH. Metode yang di pergunakan dalamPenelitian ini adalah Metode Deskriptif Kualitatif. Subjek dalam penelitian ini adalah berjumlah tigaorang, yang terdiri dari: Ketua Pelaksana PKH tingkat Kecamatan Sepauk, Pendamping PKH, RumahTangga Penerima PKH di Kecamatan Sepauk, Pengumpulan data-data di lakukan dengan teknikWawancara dan Observasi juga dengan Studi Dokumentasi. Berdasarkan Hasil Penelitian bahwaImplementasi Program Keluarga Harapan Di Kecamatan Sepauk Kabupaten Sintang dalam Mekanismepenyaluran, tataran penyaluran PKH Kecamatan Sepauk berpedoman pada Peraturan Direktur JendralPerlindungan dan Jaminan Sosial Nomor 03/3/BS.01.02/4/2020, Tentang Mekanisme Penyaluranbantuan sosial Program Keluarga Harapan. Koordinasi, Pelaksanaan penyaluran PKH berkoordinasidengan pihak Depsos Kabupaten Sintang dan mereka juga pihak Kecamatan selalu memberikaninformasi kepada kepala desa. Laporan Pertangungjawaban, lebih jelasnya yang melakukan pelaporanadalah dari Pedamping Program Keluarga Harapan Kecamatan Sepauk Melakukan pelaporan kepadapihak Dinas Sosial Kabupaten Sintang. Sumber daya pelaksana PKH, Petugas Pelaksana berjumlahsatu orang petugas bertangung jawab terhadap empat Desa di Wilayah Kecamatan Sepauk.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.012 |
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".