New Direction to the Sustainable Development Goals in Job Creation Bill in Indonesia
Bibliographic record
Abstract
United Nations Sustainable Development Goals (UN SDGs) deliver a long list for UN Member countries to do, especially in the legal and regulatory frameworks. UN SDGs contain many kinds of human needs aspect, and it is not easy to regulate all through the conventional legislative process. It raises the needs of the omnibus bill use. This article studied the opportunities and the urgency of the omnibus bill use to solve this problem. The author uses a doctrinal method with literature studies to examine how SDGs can be obtained with the use of the omnibus bill. The omnibus bill has the potential to help countries achieve the UN SDGs. In the implementation stage, the author found that it is a normal phenomenon that a political will contains economic interests in the adaptation process, likewise, with the omnibus bill case in several countries. However, it does not erase the fact that there has been a wave of resistance in its implementation due to people's unpreparedness in accepting changes brought by the political policies that are contained in the controversial omnibus bill. Therefore, the government must ensure that the omnibus bill is open to the change opportunities demanded by the people to improve the omnibus bill.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".