IMPLEMENTASI KEBIJAKAN PENANGANAN PANDEMI COVID-19 DI KOTA SUKABUMI (STUDI KASUS PEMBATASAN SOSIAL BERSKALA BESAR)
Bibliographic record
Abstract
Tujuan penelitian ini adalah untuk mengetahui Implementasi kebijakan penanganan pandemi covid-19 di Kota Sukabumi (Studi Kasus Pembatasan Sosial Berskala Besar). Metode penelitian yang digunakan dalam penelitian ini adalah pendekatan kualitatif dimana dalam penelitian yang dilakukan bersifat deskriptif yang menggambarkan fenomena sebenarnya dari kejadian di lapangan. Teknik pengumpulan data menggunakan teknik Wawancara, Observasi, dan dokumen yang terkait dengan penelitian. Teknik analisis data dalam penelitian ini dilakukan secara kualitatif. Penelitian ini menggunakan empat dimensi implementasi kebijakan dari Edward III meliputi sumber daya, komunikasi, disposisi, dan struktur birokrasi. Dari penelitian diperoleh kesimpulan bahwa sejauh ini pegawai Pemerintah Kota Sukabumi memiliki keahlian yang mumpuni dan sesuai dengan yang dibutuhkan dalam menjalankan pro-gram-program yang ada. Untuk sarana prasarana yang dimiliki oleh in-stansi belum dapat berjalan dan mendukung sepenuhnya dalam im-plementasi ini.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 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".