Comparative Analysis of Child Protection Laws: Lessons for Indonesia in Safeguarding Children with Disabilities from Sexual Abuse
Bibliographic record
Abstract
The issue of sexual abuse against children with disabilities remains a critical concern globally, with such children being more vulnerable than their peers without disabilities. This paper examines the gaps and challenges in child protection laws, with a particular focus on Indonesia, through a comparative analysis of international frameworks. While Indonesia has made progress through reforms like the 2016 Child Protection Law and the 2022 Sexual Violence Crime Law, it still faces challenges in addressing the specific needs of children with disabilities. These children often struggle with communication barriers, cognitive limitations, and societal stigma, which prevent them from reporting abuse and hinder justice. Drawing on the experiences of countries like the United Kingdom, Australia, and Canada, the paper identifies key lessons for strengthening Indonesia’s legal frameworks. The UK’s comprehensive approach to child protection, Australia’s rights-based system, and Canada’s integration of Indigenous perspectives provide valuable insights for Indonesia. The paper argues that Indonesia should expand its legal protections, enhance access to justice, and adopt a multidisciplinary approach to safeguarding children with disabilities. It emphasizes the importance of cultural sensitivity and the use of technology in improving child protection systems. Ultimately, the paper calls for a shift towards a rights-based model, ensuring that children with disabilities are empowered and protected in Indonesia and beyond.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".