Sexual violence prevention is missing in teacher education: perspectives of teacher candidates on prevention education
Bibliographic record
Abstract
This article derives from part of a larger study on sexual violence prevention in teacher education that analyses the narratives of fifteen teacher candidates in an Ontario university. It begins by providing a rationale for the research, which engages emerging teachers as key stakeholders in prevention education. Narrative inquiry was conducted to understand the experiences of teacher candidates who were troubled by the programme’s lack of education and training on sexuality education and sexual violence prevention. Teacher candidates reflected that their first education about consent and sexual violence occurred in a postsecondary rather than an elementary or secondary school context. As part of the teacher certification programme, participants felt entitled to learn about sexuality education methodologies and sexual violence prevention education. As emerging teachers, they expressed the desire to know how to teach young people about sex and consent, healthy relationships, boundaries, and the sociopolitical contexts of sexual violence, as well as how to sensitively respond to disclosure. Most pointedly, participants understood the power that effective sexuality education by trained teachers may have in reducing victimisation, thereby contributing to educational equity. Findings are discussed in relation to the literature on feminist understandings of sexuality education and sexual violence prevention.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".