#MeToo in British schools: Gendered differences in teenagers’ awareness of sexual violence
Bibliographic record
Abstract
This article explores how British secondary school students responded to and made sense of the rising public awareness of sexual violence in British society that emerged during lockdowns for COVID-19. Based on the findings from a 2021–2022 study conducted in five secondary schools, the article explores the gendered discrepancies in girls’ and boys’ awareness of violence against girls and women. In particular, it examines how the youth participants in this study responded to two related media stories during lockdown: the news of Sarah Everard’s kidnapping and murder by a police officer and the viral spread of sexual abuse testimonies on the ‘Everyone’s Invited’ Instagram page and website. The article demonstrates how girls were more likely to experience, recognize, and discuss sexual violence, in part due to feminist consciousness raising during lockdown via digital technologies like Instagram and TikTok. Although some boys did recognize the problem of violence against women, in general, they were much less aware of Sarah Everard’s murder and Everyone’s Invited and were prone to absorbing manosphere-like discourses around false rape accusations In focus groups, some boys deployed a defensive masculinity and adopted a discourse of male victimhood, which denied the scale and scope of violence against girls and women. However, through involving boys in focus group discussion with both us and their male peers about power and privilege, progress was made in challenging and counteracting rape myths and anti-feminist male victimization narratives.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".