Pelayanan Surat Keterangan Pindah Bagi Penduduk Migran Di Kabupaten Bogor Provinsi Jawa Barat
Bibliographic record
Abstract
Dinas Kependudukan dan Pencatatan Sipil (Disdukcapil) Kabupaten Bogor memiliki peran penting dalam mengelola administrasi kependudukan karena jumlah migran yang membutuhkan pelayanan administrasi kependudukan terkait dengan kepindahannya cukup tinggi. Penelitian ini bertujuan menggambarkan pelayanan administrasi kependudukan bagi penduduk migran di Kabupaten Bogor dan berbagai faktor yang mempengaruhinya. Metode penelitian yang digunakan dalam penelitian ini adalah Deskriptif Kualitatif. Pengumpulan data dilakukan dengan observasi, wawancara, dan dokumentasi. Teknik analisis data dilakukan dengan beberapa tahapan yaitu mempersiapkan data, membaca keseluruhan data, penyajian data, mengorganisasikan data, menganalisis data, mendeskripsikan data, dan pembuatan interpretasi/penarikan kesimpulan. Hasil penelitian menunjukkan bahwa dalam memberikan pelayanan administrasi kependudukan bagi penduduk migran, Disdukcapil Kabupaten Bogor telah memenuhi dimensi-dimensi pelayanan yang baik, dipenuhi melalui berbagai upaya diantaranya program One Day Service (ODS), tersedianya Standar Operasional Prosedur (SOP) pelayanan yang jelas, dan pemanfaatan teknologi untuk mempercepat proses pelayanan, serta menciptakan lingkungan yang nyaman dan efisien bagi penduduk migran yang mengurus dokumen kependudukan. Kata kunci: Pelayanan publik, Administrasi Kependudukan, Penduduk Migran.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".