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Record W4320007493 · doi:10.35631/ijlgc.730011

CYBERBULLYING IN MALAYSIA: AN ANALYSIS OF THE EXISTING LAWS

2022· article· en· W4320007493 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

VenueInternational Journal of Law Government and Communication · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLegal and Social Justice Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNewspaperLegislationPolitical scienceLawCriminologyAdvertisingPublic relationsPsychologyBusiness

Abstract

fetched live from OpenAlex

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.035
GPT teacher head0.337
Teacher spread0.302 · 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