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Cyberbullying and Internet Safety

2015· book-chapter· en· W2475842294 on OpenAlex
Deirdre M. Kelly, Chrissie Arnold

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in media, entertainment and the arts (AMEA) book series · 2015
Typebook-chapter
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHarmCovertFraming (construction)CounterintuitiveHarassmentCrowdsSocial psychologyHeterosexismPsychologyCriminologyComputer securityEngineeringComputer science

Abstract

fetched live from OpenAlex

The chapter considers cyberbullying in relation to Internet safety, concentrating on recent, high quality empirical studies. The review discusses conventional debates over how to define cyberbullying, arguing to limit the term to repeated, electronically-mediated incidents involving intention to harm and a power imbalance between bully and victim. It also takes note of the critical perspective that cyberbullying—through its generic and individualistic framing—deflects attention from the racism, sexism, ableism, and heterosexism that can motivate or exacerbate the problem of such bullying. The review concludes that: (a) cyberbullying, rigorously defined, is a phenomenon that is less pervasive and dire than widely believed; and (b) cyber-aggression and online harassment are more prevalent, yet understudied. Fueled by various societal inequalities, these latter forms of online abuse require urgent public attention. The chapter's recommendations are informed by a view of young people as apprentice citizens, who learn democratic participation by practicing it.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.964
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.266
Teacher spread0.252 · 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