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Record W2154549635 · doi:10.1177/0143034306064547

Cyberbullying in Schools

2006· article· en· W2154549635 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.

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.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0160.003

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