Epistemic justice, continuum thinking, and discomfort pedagogy: Teacher candidates engage with doing equity work through the vantage point of sexual violence prevention
Bibliographic record
Abstract
This article explores how teacher candidates engage with sexual violence prevention as a site of equity learning within a year-long initial teacher education course in Ontario, Canada. Grounded in feminist pedagogy and structured around service learning, the course positioned sexual violence as a relational, emotional, and systemic issue requiring intentional, sustained engagement. Drawing on reflective journal entries that teacher candidates wrote throughout the year, the study employed feminist critical discourse analysis to examine how participants made sense of their learning. The analysis produced three discursive themes: enacting a pedagogy of discomfort, doing epistemic justice to sexual violence, and adopting continuum thinking. Findings reveal how teacher candidates moved beyond reductive understandings of consent and sexual violence to engage with the broader social and institutional conditions that scaffold harm. The study demonstrates the transformative potential of integrating experiential learning and trauma-informed frameworks to prepare educators to engage in meaningful, continuous equity work across educational contexts, while also exemplifying how the sexual violence curriculum extends to other equity issues in preparing students to enact practical strategies for social and institutional change.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".