Criminal Liability by the Pharmaceutical Industry on the Use of Precursors for Illicit Narcotics in Indonesia: A Review
Bibliographic record
Abstract
Purpose of the study: the aim of this study was to review the law enforcement regarding precursors for manufacturing narcotic drugs in Indonesia. Methodology: This study used normative legal research, which used the law as positive norms that regulates human life, it used several approaches, that were examined various rules of law as well as case approach. The data was collected through literature studies. Main Findings and Applications of this Study: In Indonesia, the highest regulation in the crime of narcotics is based on the Law of the Republic of Indonesia Number 35 of 2009 concerning Narcotics. The aims of this regulation are to protect the public from precursor’s abuse to narcotics; preventing and eradicating illicit traffic of precursors of narcotics; as well as preventing leaks and irregularities. Novelty: The pharmaceutical industry as a legal entity has the possibility to conduct criminal action such as using precursor for illicit narcotic and if it is proved to be in violation, it will be punished. Furthermore, it requires integration by involving national, regional and international coordination to prevent this criminal liability
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".