A Criminological Outlook of Cyber Crimes in Sexual Violence Against Children in Indonesian Laws
Bibliographic record
Abstract
The purpose of this study is to analyze the cybercrimes in sexual violence against children in Indonesian laws. Act Number 11 of 2008 concerning Information and Electronic Transactions, concerning cybercrime specifically regulated in Indonesia. Of the various types of cybercrime that occur in Indonesia, it is interesting to study the vacuum norms governing cyber pornography carried out on children in Act Number 11 of 2008 concerning Information and Electronic Transactions. The crime can be said as violence against children whose punishment can be aggravated as stipulated in the Child Protection Act. This research is a normative legal study by examining the absence of norms in the ITE Law regarding sanctions imposed on perpetrators of child abuse in cyberspace. The study was conducted by using normative research methods so that utilizing primary legal materials such as the Criminal Code, Child Protection Act, Pornography Law, and Electronic Information and Transaction Law. Based on the collection and analysis of the legal material, the results showed that there is a need for criminal penalties for cyber pornography against children. This is done by considering the impact of the crime on the development of children and aims that the perpetrators deter and prevent similar crimes. The results practically contribute that the government is expected to play an active role in continuing to provide protection and assistance for psychological recovery from victims.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".