Creating a canon for change: how teacher candidates demonstrate readiness to reckon with rape culture through reading trauma literature
Bibliographic record
Abstract
This paper explores how teachers in training co-created a canon of texts for teaching about trauma issues, including sexual violence. This paper represents a piece of a larger feminist study where 23 teacher candidate participants took up readings in a sexual trauma text set and responded to pedagogy for teaching such texts with Canadian adolescent literacy learners. Overall, the data strongly indicated that many participants prioritized promoting social action in their emerging pedagogies, including anti-rape efforts. Discourses of readiness to combat rape culture especially surfaced, signalling that overwhelmingly, participants were authoring themselves as educators who prioritize creating community and enacting resistances to oppressions in some way. As such, a key finding examined in this paper was how participants collectively built on the initial corpus of trauma texts in the study’s text set that they advocated for or planned to teach in their future education careers.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".