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Record W2792818025 · doi:10.1177/0044118x18757150

Gendered and Sexualized Bullying and Cyber Bullying

2018· article· en· W2792818025 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueYouth & Society · 2018
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsMcGill UniversityYork UniversityUniversity of Toronto
Fundersnot available
KeywordsHarassmentInvisibilityAggressionSocializationPsychologyPoison controlSocial psychologySuicide preventionGender studiesCriminologySociology

Abstract

fetched live from OpenAlex

Drawing on semistructured interviews with Canadian Grade 4 to 12 students, this article uses a feminist lens to explore gendered and sexualized bullying and cyberbullying among children and youth. Our findings indicate that while boys’ roles and behaviors were frequently made invisible, girls were typically spotlighted, blamed, and criticized. Girls’ experiences were often minimized and normalized by peers and linked to gender norms and stereotypes that were largely invisible to participants. The central theme of invisibility emerged, which encompassed and interconnected the three subthemes: (a) gendered stereotyping, (b) spotlighting girls, and (c) gender surveillance and policing. Gendered and sexualized bullying and cyberbullying were found to be part of a socialization process wherein girls come to expect gender-based aggression, violence, and inequality in their lives. This article makes explicit how bullying and cyberbullying are linked to societal norms that put girls at risk of harassment, violence, abuse, and discrimination.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.835

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 studies0.0010.000
Scholarly communication0.0000.000
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 head0.041
GPT teacher head0.309
Teacher spread0.268 · 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