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Record W2059835956 · doi:10.1080/10926771003788979

Cyberbullying in High Schools: A Study of Students' Behaviors and Beliefs about This New Phenomenon

2010· article· en· W2059835956 on OpenAlexaffabout
Qing Li

Bibliographic record

VenueJournal of Aggression Maltreatment & Trauma · 2010
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPhenomenonNothingPsychologySocial psychology

Abstract

fetched live from OpenAlex

This study explores high school students' beliefs and behaviors associated with cyberbullying. Specifically, it examines this new phenomenon from the following four perspectives: (a) What happens after students are cyberbullied? (b) What do students do when witnessing cyberbullying? (c) Why do victims not report the incidents? and (d) What are students' opinions about cyberbullying? Data were collected from 269 Grade 7 through 12 students in 5 Canadian schools. Several themes have emerged from the analysis, which uncovers some important patterns. One finding is that over 40% would do nothing if they were cyberbullied, and only about 1 in 10 would inform adults. Students feel reluctant to report cyberbullying incidents to adults in schools for various reasons, which are discussed in depth.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.130
Threshold uncertainty score0.259

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0050.003
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.003
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 head0.019
GPT teacher head0.328
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations228
Published2010
Admission routes2
Has abstractyes

Explore more

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