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Record W2154549635 · doi:10.1177/0143034306064547

Cyberbullying in Schools

2006· article· en· W2154549635 on OpenAlexaff
Qing Li

Bibliographic record

VenueSchool Psychology International · 2006
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPsychologyCyber bullyingSuicide preventionHuman factors and ergonomicsPoison controlInjury preventionDevelopmental psychologySocial psychologyClinical psychologyMedicineThe InternetMedical emergency

Abstract

fetched live from OpenAlex

This study investigates the nature and the extent of adolescences’ experience of cyberbullying. A survey study of 264 students from three junior high schools was conducted. In this article, ‘cyberbullying’ refers to bullying via electronic communication tools. The results show that close to half of the students were bully victims and about one in four had been cyber-bullied. Over half of the students reported that they knew someone being cyberbullied. Almost half of the cyberbullies used electronic means to harass others more than three times. The majority of the cyber-bully victims and bystanders did not report the incidents to adults. When gender was considered, significant differences were identified in terms of bullying and cyberbullying. Males were more likely to be bullies and cyberbullies than their female counterparts. In addition, female cyberbully victims were more likely to inform adults than their male counterparts.

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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.345
Teacher spread0.325 · 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

Citations944
Published2006
Admission routes1
Has abstractyes

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