Politica of Criminal Law; The Importance of Revision of Mineral and Coal Law: Who Is Benefitted
Bibliographic record
Abstract
Minerals and coal are state owned assets that need to used wisely, although the People's Representative Council (DPR) is yet to ratify them for the 2020-2025 period during the COVID-19 pandemic that has ravaged the world, including Indonesia. The review of the Mineral and Coal has caused many draw backs because it does not follow the principle of balancing and sharing regional authority. This study discusses the principle of strict liability as stipulated in Law No. 3/2020 by examining the urgency for changes and who will benefit from the revision. Doctrinal research was used for analyzing the politics of criminal Law in the Mineral and Coal, both before and in its amendments. Furthermore, Law No. 4/2009 ensnares many people that carry out illegal mining because the mechanism and procedure for the popular Mining license is the same with the mining license of the companies. The content of the amendments to the Mineral and Coal only regulates the sale of 51% foreign shares, but from a criminal perspective and nominalization of criminal fines and the reduction of prison sentence, it can be seen that the new criminal concept does not have a significant meaning for business corporations, as the suspects so far have been people that have been mining in the form of community mining.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".