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Record W2130860517 · doi:10.3138/cjccj.2014.e43

Cyberbullying: Is Federal Criminal Legislation the Solution?

2015· article· en· W2130860517 on OpenAlexaffvenueabout
Patricia I. Coburn, Deborah A. Connolly, Ronald Roesch

Bibliographic record

VenueCanadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénale · 2015
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsLegislationGovernment (linguistics)Criminal justiceCriminologyInterpersonal communicationEconomic JusticePsychologyPolitical scienceSocial psychologyLaw

Abstract

fetched live from OpenAlex

Cyberbullying occurs frequently and is often a reciprocal conflict, with individual youths filling the roles of both the victim and the bully in a short period of time. Regardless of the role, involvement in cyberbullying is associated with negative outcomes and has recently been linked to the death of young people in a few cases. In an attempt to alleviate growing concerns about cyberbullying, the Canadian government passed Bill C-13 (the Protecting Canadians from Online Crime Act), which includes a prohibition on the posting of non-consensual intimate images. Due to the specific criteria in this section of the bill, it is unlikely that it will protect many youth from online victimization. Bill C-13 also criminalizes harassing or annoying behaviour conducted via electronic communication. This law may exacerbate the problem of non-disclosure, may be confusing to youth, and may result in too many youth and a disproportionate number of marginalized youth becoming involved in the criminal justice system. Alternative approaches to dealing with the conflict, such as increasing the use of empirically based programs that teach youth to resolve interpersonal conflict and encourage them to disclose incidents of cyberbullying, would be more effective than federal criminal legislation at protecting young people from online victimization.

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.

How this classification was reachedexpand

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.130
GPT teacher head0.332
Teacher spread0.203 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénaleSame topicBullying, Victimization, and AggressionFrench-language works237,207