Gendered and Sexualized Bullying and Cyber Bullying
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".