Metode Omnibus Law dalam Pembaharuan Hukum Pembentukan Peraturan Perundang-Undangan di Indonesia (Studi Perbandingan Negara Kanada, Amerika Serikat, Filipina dan Vietnam)
Bibliographic record
Abstract
The method for forming omnibus laws and regulations is relatively new to positive law for the formation of laws and regulations in Indonesia, considering overlapping regulations are one of the legal issues for reforming laws and regulations in Indonesia that need serious attention. There is a great number of laws and regulations that overlap each other, both horizontally and vertically, resulting in disharmony and legal uncertainty in the laws and regulations in Indonesia and to increase investment value and the national economy which is still relatively low when compared to other countries. This research discusses how the omnibus law concept is applied in other countries in the formation of laws and regulations; and whether the concept of the omnibus law implemented by the Government of Indonesia is in accordance with the objectives of the law and the legal reform of the formation of statutory regulations. This study uses normative research methods. The results of this study conclude that first, other countries, namely Canada, the United States, the Philippines and Vietnam have different legal reasoning, namely as a consolidated norm; increase the investment sector; and the many laws and regulations that overlap with each other and the process of forming laws and regulations is lengthy. Second, the omnibus law method in Indonesia is through Law No. 11 of 2020 on Job Creation which has been revoked by Government Regulation in lieu of Law No. 2 of 2022 does not reflect the objectives of the law (fairness, public benefit and legal certainty) and there are no principles for forming good statutory regulations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".