Exploring teacher candidates’ attitudes towards teaching sexual assault narratives
Bibliographic record
Abstract
This feminist doctoral research was designed to develop theory, innovative pedagogy, and to advance knowledge by contributing to a lack of research on teaching and learning about sexual assault narratives, rape culture, and Tarana Burke’s Me Too movement, feminist approaches that center intersectionality in teacher education, and employing feminist and critical discourse analysis and poetic inquiry. This study explored secondary English teacher candidates’ responses to learning about teaching assault narratives and demonstrates how assembling a diverse selection of such stories together in a trauma text set – not to compare them, but to create an atmosphere of considered confrontation – engenders promising new ways for educators to consider how they might reframe the secondary English classroom as a site for dynamic solidarity and enacting resistance(s) against rape culture. Future educators will go on to cultivate dynamic literacy learning and as such, a central goal of this project was to create space for exploring the difficult or radical knowledge(s) that emerge from sexual assault narratives, and to consider how literature can function as a vehicle for interrogating rape culture. Additionally, this project offered modeling and space for candidates to tackle intense subject matter such as sexual trauma in the English literature classroom. To do this work, I first joined two sections of a UBC Bachelor of Education course as a guest, then a guest lecturer to run workshops on teaching sexual assault narratives. Next, I recruited 23 teacher candidate participants to conduct individual interviews and focus groups to explore their responses to the pedagogy and texts. I then employed a two-pronged methodological approach with both feminist critical discourse analysis and feminist critical poetic inquiry for data analysis. The findings show that teacher candidates can have complex responses to sexual assault narratives and the challenges of teaching them. However, they overwhelmingly demonstrated commitments to anti-rape efforts by showcasing readiness and willingness to engage future students in social justice work generally and more specifically, teach about rape culture. Overall, participants were excited to create and facilitate secondary English classrooms as hopeful places for necessary paradigm shifts and resistance to violence of all kinds.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".