LEGAL CHALLENGES OF ADOPTING AGE-VERIFICATION TECHNIQUES FOR THE PROTECTION OF MINORS ON THE INTERNET IN MALAYSIA
Bibliographic record
Abstract
Corporations in the form of Limited Liability Companies in Indonesia are regulated in Limited Liability Company Law No. 40 of 2007 concerning Limited Liability Companies, this Law regulates the liability of corporations and/or shareholders who commit acts against the law, but the liability that can be asked of shareholders does not exceed existing shares. This study uses normative legal research methods. The data used are secondary data consisting of primary legal materials, secondary legal materials, and tertiary legal materials. For data analysis, the qualitative jurisdictional analysis method was used. From this research, it can be found that law enforcement against shareholders who commit acts against the law can be upheld and the outcome is that the action against the law which was originally a civil action and then turned into a criminal act. By using the Piercing, the corporate veil doctrine, shareholders who commit acts against the law can be sentenced to criminal and all their assets to cover the financial losses of the state due to their actions. It is universally applied on the basis of fraudulent acts carried out to rake in personal profit and by implementing civil forfeiture or civil recovery, the proceeds of crimes committed by shareholders are likely to be returned.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".