Oversharing and its Impact for Children: A Comparative Legal Protection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study aims to identify the potential threat for children resulting from the intense personal data overshare in cyber-space and examine its legal protection. This study uses descriptive qualitative with a case, comparative and conceptual approaches. The primary legal material used in this study is Law No. 44 of 2008 on Pornography (Pornography law) and Law No. 11 of 2008 on Information and Electronic Transaction (ITE law), last amended by law No. 19 of 2016 on the Amendment of Law No. 11 of 2008. The secondary legal material used in this study includes books, journals, and related articles. The result shows that threats resulting from the overshare lead to various offenses like cyberbullying, pedophile threats, identity theft, identity manipulation, deepfake, and cyberstalking. In Indonesia, the legal protection of the children cyber offense victim does not specifically regulate. While learning from several countries, such as the U.S, Canada, France and the U.K, have stipulated the provision regarding children’s protection, especially in cyber-space. Through this study, the author proposed the appropriate regulation to tackle the issues of cyber offense for children in Indonesia by complementing the existing regulation regarding the limitation of oversharing of data in the cyber-space.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it