KEBIJAKAN HUKUM DI TENGAH PENANGANAN WABAH CORONA VIRUS DISEASE (COVID-19)
Bibliographic record
Abstract
Penanganan Covid-19 secara nasional merupakan kesatuan tindakan yang lahir dari kebijakan strategis komprehensif. Kebijakan ini harus mengatasi kondisi terkini dan mengantisipasi dampaknya di kemudian. Upaya-upaya yang saat ini dilakukan oleh pemerintah adalah: kebijakan social distancing/physical distancing, perlindungan bagi tenaga kesehatan sebagai garda depan, pembatasan sosial berskala besar, transparansi pemerintah dalam penanganan pandemi Covid-19, validitas data hasil pemeriksaan. Untuk penanganan wabah Covid-19 ini, penegakan hukum menjadi salah satu langkah yang dipilih oleh pemerintah. Aparat kepolisian bertugas dalam membubarkan kerumunan massa, menangani penyebar berita bohong atau hoax, serta penimbun bahan pokok. Selain itu pihak kepolisian juga telah mempersiapkan ancaman pidana bagi masyarakat yang melanggar, sanksi tersebut terdapat dalam Maklumat Kapolri Nomor Mak/2/III/2020 tentang Kepatuhan terhadap Kebijakan Pemerintah dalam Penanganan Penyebaran Virus Corona (Covid-19) dan bentuk pelanggaran atau kejahatan yang mungkin terjadi selama PSBB dalam Surat Telegram Kapolri Nomor ST/1098/IV/HUK.7.1./2020.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".