The SOLUSI POTRET PROBLEMATIKA MATERI MUATAN REGULASI DALAM PENANGANAN COVID-19 DI INDONESIA
Bibliographic record
Abstract
The COVID-19 pandemic has had a tremendous impact, ranging from the economic crisis to public health, which is the government's focus in minimizing the impact of the Covid-19 pandemic. The type of research used in this research is juridical-normative. And the purpose of this research, namely; 1) describe the regulations issued by the Central and Regional Governments in dealing with the Covid-19 Pandemic, 2) and describe solutions to overcome regulatory problems issued by the Central and Regional Governments during the Covid-19 Pandemic. The government in issuing several regulations looks inconsistent, for example; the difference in the definition of PSBB as regulated in PP No. 21 of 2020 with that regulated in the Quarantine Law. Then, regarding the Instruction of the Minister of Home Affairs Number 15 of 2021 which is considered to have neglected the regulations above. Problems with existing regulations, the government needs to break the chain of spread of the Covid-19 pandemic with the product of regulations based on the Tiered Law Theory by Hans Nawiasky. This theory then when associated with problems in Indonesia can make Article 34 paragraph (3) of the 1945 Constitution and Law no. 6 concerning Health Quarantine is a reference for the government in formulating the rules under it, in matters relating to regulations during the Covid-19 pandemic so that it becomes a solution in overcoming the regulatory problems of handling the Covid-19 pandemic.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".