A Comparative Analysis of Cyberbullying and Cyberstalking Laws in the UAE, US, UK and Canada
Bibliographic record
Abstract
Bullying and stalking through cyberspace have become serious phenomena in the Internet era, impacting mainly young users and teenagers. Many tragic incidents have occurred, especially in the West, including self-harm and suicide due to these problems. To protect the victims many countries such as the United Arab Emirates (UAE), the United States (US), the United Kingdom (UK) and Canada have codified laws dealing with cyber-crimes, including cyber-harassment. To determine the adequacy of such laws in addressing these issues, we present in this paper a legal analysis of the existing anti-bullying and stalking laws in the UAE, US, UK, and Canada. The purpose is to gain perspective on the characteristics of the laws and their ability to protect society from various forms of crimes associated with cyberbullying and cyberstalking. The paper also presents recommendations to help combat cyberbullying and cyberstalking and protect our youth from these issues.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".