Gender-Based Victimization and Gender Norms Among Adolescent Girls: A Latent Profile Analysis
Bibliographic record
Abstract
Distinct forms of gender-based victimization (e.g., sexist experiences, slut-shaming, sexual harassment, and homophobic bullying) are often studied in isolation among adolescent girls, yet some girls are likely to experience multiple forms simultaneously. Researchers have theorized that these forms of gender-based victimization can function to regulate culturally acceptable gendered expression and behavior, but more empirical work is needed to understand these linkages. We had two aims: (a) to identify profiles of gender-based victimization (i.e., sexist experiences, slut-shaming, sexual harassment, and homophobic bullying) among adolescent girls and (b) to understand how these profiles are associated with girls’ relationships to gender norms (i.e., gender expression, internalized sexism, internalized sexualization, and sexual identity). We analyzed data from a larger study on adolescents’ peer experiences and attitudes in Québec, Canada ( n = 203 girls, M age = 15.4 years, 30% racialized minorities). We used latent profile analysis to identify patterns of gender-based victimization experiences, and then we used latent variable multinomial regression to test the association between the profiles of victimization and girls’ relationships to gender norms. The best model was a two-profile solution: High Victimization and Low Victimization. Compared to the Low Victimization group, participants in the High Victimization group were more likely to identify as sexual minorities and report higher levels of internalized sexualization. Our findings support intervention and prevention approaches that focus on the links between multiple forms of gender-based violence and that acknowledge sexualization and sexual minority status as risk factors. Ultimately, our results suggest that one function of gender-based victimization is policing gender norms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".