‘Everything they think they know, they should forget’: how trans school workers respond to transphobic discourses
Bibliographic record
Abstract
This article explores how trans school employees redeploy, trouble, challenge, and refuse prevalent discourses about transness and education. Drawing from interviews with 100 school workers in Canada and the United States, we employ Coleman’s (2023) concept of ‘narrative repair’ to consider participants’ responses to an interview question asking what they would like the public to know about their workplace experiences. We show that workers in our study primarily drew from four overarching narratives about the relationship between their transness and their work in schools: 1) trans school workers face increased difficulty; 2) trans school workers have unique strengths; 3) trans school workers are no different from other school workers; and 4) the experiences of trans school workers defy narrativization. Our findings deepen understandings of how broader cultural discourses influence how teachers narrativize their professional experiences.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.034 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".