The Pathway of Adopting Omnibus Law in Indonesia's Legislation: Challenges and Opportunities in Legal Reform
Bibliographic record
Abstract
The omnibus law model has become a new method of legislative drafting in Indonesia, first applied to the Job Creation Law and later enacted as Law 11/2020. While there were no implicit guidelines in Legislative Drafting Law 12/2011, this adoption was imported from several countries like the United States and Ireland to simplify regulations before the method was subsequently formalized and included in Legislative Drafting Law 13/2022. This paper explored the pathway and dynamics of the omnibus law adoption in Indonesia's law-making procedure and analyzed its further impacts on whether such a method has fruitfully improved the quality of the enacted regulation in establishing a more friendly investment policy. Through doctrinal method, this study showed that the opportunity to apply the omnibus model in Indonesia depends on the effectiveness, success, and benefits of respective regulations. In contrast, the application of the omnibus law model should respect democratic principles and avoid public harm. As shown in three different countries, i.e., Indonesia, the United States, and Canada, public concerns on lack of participation should be taken seriously to hinder undemocratic ends through "democratic" means. Alternatively, accountability of the drafting process should be considered a priority. In summary, the increasing trend of adopting the omnibus model should be first adopted and promulgated through legislative products whose promulgation must be with a formidable law-making procedure.
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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 | 0.006 | 0.006 |
| 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 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".