A Narrative Inquiry of Teacher Candidate Experiences of Sexual Violence Prevention Education
Bibliographic record
Abstract
Sexual violence scholars and practitioners in postsecondary and community contexts have long called for K-12 education to join them to take on the challenge of sexual violence prevention, citing school as simultaneously an important factor in addressing sexual violence and a place where it occurs. Meanwhile, teacher education scholars have pointed out there are missed opportunities to provide training for teachers at the earliest point in their training. Teacher candidates’ concurrent roles as an at-risk population and as educators and leaders entering schools puts them in an interesting space that is worth investigating. This research uses narrative inquiry to explore teacher candidates’ experiences of sex education during their time in school, their emerging understandings of sex and sexual violence, and their experiences learning about sexual violence prevention before and during their teacher education. Recruitment took place at a medium sized university in Southwestern Ontario, Canada, and data consisted of interviews with fifteen first- and second-year teacher candidates of various specialties and educational backgrounds. Narrative analysis was informed by feminist epistemology and theory, as well as the tenets of feminist methodology and narrative inquiry. Findings indicate the following: 1) experiences of inadequate sex education from teacher candidates’ time in K-12 education; 2) learning about sex and sexual violence in the margins of the curriculum; 3) experiences of gendered, risk-based discourses and narrow definitions of sex didn’t prepare participants for sexual citizenship; 4) narratives of fragmented understandings of consent and the continuum of sexual violence; 5) participants’ own K-12 teachers’ discomfort with teaching sex education and addressing sexual violence; 6) stories of not having learned about sexual violence until postsecondary school; 7) teacher training did not prepare participants’ to teach about sex education, nor to address sexual violence prevention; 8) the desire to learn about consent and sexual violence in participants’ new roles as teachers entering school and education systems. These narrative themes are discussed in relation to feminist understandings of sexual violence and prevention education, narrative meaning making in teacher education, knowledge mobilization, and directions for future research.
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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 | 0.006 | 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 | 0.012 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".