Nonbinary Beginning Teachers: Gender, Power, and Professionalism in Teacher Education
Bibliographic record
Abstract
Background/Context: In recent years, Canadian and U.S. schools have increased efforts to recognize gender diversity and reduce gender-based harassment, in large part because a growing number of young people are coming out as transgender or nonbinary in adolescence. However, little research explores nonbinary teachers’ experiences or investigates barriers to their entry into the profession. Purpose: This article begins to fill this gap by showing how six nonbinary beginning teachers navigated gender expectations, worked to appear professional, and negotiated racial and gendered power dynamics in their initial teacher education and preservice teaching. Participants: Participants include six nonbinary preservice teachers of diverse gender expression and racial and class backgrounds who were enrolled or had recently completed teacher education in North America when the study was conducted in 2018. Research Design: This qualitative study employed in-depth, phenomenological interviews. This article uses Sara Ahmed’s concept of the “willful subject” to consider how participants negotiated the relationship between their gender identities as nonbinary people and their nascent professional identities as teachers. Conclusion: These beginning teachers expressed concern about succeeding in their teacher education programs and worried about how others perceived them because of the expectation of normative gender implicit in teaching’s professional norms. This expectation was enforced by the profession’s gatekeepers more than by K–12 students and their families, who participants generally described as hospitable or indifferent to having a nonbinary teacher. If the profession is to genuinely welcome gender diversity, it must recognize and work to deconstruct its own gender normativity.
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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 | 0.004 |
| 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 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".