“The student drives the car, right?”: Transgender students and narratives of decision-making in schools
Bibliographic record
Abstract
This paper draws on interviews with 69 educators in four school districts in Canada who worked with trans students as they transitioned at their school to analyze how students’ involvement in decision-making processes were featured in participants’ stories. I focus specifically on the “student in charge” narrative that appeared prominently in my interviews and frames the young person as the expert on their own life and thus the person best positioned to set the pace and shape what the transition looks like, with adults following their lead. While this narrative seems to offer a hopeful intervention into the systems of cisnormativity that structure schools, I argue that a closer look at how this narrative functions in the talk of educators reveals that it is often undermined by dominant discourses of youth, safety, and choice that limit its transformative potential and often bolster cisnormative assumptions. As a result, opportunities for advocating for trans students are missed, as are possibilities to transform educational spaces in a sustainable way that would allow all students to attend schools without the pressures of gender and sexual conformity.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.037 | 0.054 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| 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".