“I Assumed It Was a Much Safer Place Than It Really Is”: Nonbinary Educators’ Strategies for Finding School Jobs
Bibliographic record
Abstract
People who are nonbinary—one of many kinds of trans identity that do not fit neatly within a man/woman binary—face particular challenges when seeking employment in P–12 schools, which have historically been places where rigid gender norms are strictly enforced. This paper draws on semistructured interviews conducted in 2018 to explore how 16 nonbinary educators navigated the process of finding, securing, and keeping jobs in Canadian and American schools. I found that most participants were concerned about securing a job or potentially losing their job or their safety at work because others might be inhospitable to their gender identity or expression. At the same time, participants had strategies to ensure that they found and kept jobs they were comfortable with, such as investigating a school’s support for queer and trans people, forging positive relationships with administrators and staff, and presenting their gender in particular ways during the hiring process. This study illustrates the limitations of individualistic, tokenizing forms of trans inclusion and reveals the continued prevalence of gender normativity in schools, despite a rapidly shifting gender landscape. While trans inclusion, at least on the surface, may be a selling point for some schools, trans people continue to face barriers when the underlying structures that privilege White, middle-class, cisgender, and heteronormative gender expression remain intact. I argue that, if trans people are to be fully supported in the education workplace, an intersectional and broadly transformative approach to gender justice is necessary.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".