CYBERBULLYING IN MALAYSIA: AN ANALYSIS OF THE EXISTING LAWS
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
Cyberbullying incidents have shocked the world, particularly the infamous suicide incident in 2012 involving a 15-year-old victim, Amanda Todd, in Canada. Malaysia is currently facing the same issue and two suicide cases of cyberbullying victims involving school children were reported in 2019 and 2020. The global statistics among 28 countries indicated that Malaysia was ranked sixth in the world and second among the Asia countries in cyberbullying. As such, this paper aims to identify the law regulating such incidents in Malaysia. The methodology used in this paper is library research by referring to legislation, journals, books, conference papers, newspapers, and other periodicals. It was observed that there is no existing legal provision specifically to tackle on cyberbullying cases in Malaysia. Therefore, a new law is needed to address the issues of cyberbullying.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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