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A Comparative Analysis of Cyberbullying and Cyberstalking Laws in the UAE, US, UK and Canada

2019· article· en· W3011201588 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicStalking, Cyberstalking, and Harassment
Canadian institutionsnot available
Fundersnot available
KeywordsStalkingCyberspaceHarmCriminologyHarassmentPolitical scienceThe InternetLawCybercrimeInternet privacySociologyComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.293
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations9
Published2019
Admission routes1
Has abstractyes

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