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Record W2111489762 · doi:10.37119/ojs2009.v15i2.57

"you were born ugly and youl die ugly too": Cyber-Bullying as Relational Aggression

2013· article· en· W2111489762 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuein education · 2013
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAggressionCovertIntervention (counseling)FriendshipPsychologyHarmSocial psychologyInterpersonal communicationDevelopmental psychology

Abstract

fetched live from OpenAlex

Cyber-bullying increasingly is becoming a problem for students, educators and policy makers. In this paper, we consider cyber-bullying as a form of relational aggression; that is, behaviour designed to damage, harm or disrupt friendship or interpersonal relationships through covert means. We draw on the findings from a study of students in Grades 6 through 9, conducted in five schools, in a large ethnically diverse metropolitan region of British Columbia, Canada, to demonstrate the interconnection between cyber-bullying and relational aggression. Consistent with the relational aggression framework, girls were found more likely than boys to participate in these behaviours. We conclude that intervention strategies should consider gender differences and also aim at changing the trajectory of relational aggression to providing relational support and care.Keywords: cyber-bullying; relational aggression; intervention strategies; gender differences

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.310
Teacher spread0.292 · 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