Gender normativity in teacher education : a critical participatory action research study with trans and gender nonconforming preservice teachers in Canada
Bibliographic record
Abstract
This dissertation explores how a group of seven trans and/or gender nonconforming preservice teachers from various programs in British Columbia and Ontario experienced, developed their analysis of, and created a resource to challenge gender normativity in teacher education through critical participatory action research. Drawing from transcripts of monthly meetings, interviews with each participant before and after the six months when the group met, participant art, and memos I wrote throughout the process of planning and facilitating the study and building on the analysis developed by the group, I suggest that gender normativity is primarily enacted implicitly in Canadian teacher education programs. Many Canadian teacher education programs declare themselves inclusive of all candidates and explicitly teach about supporting gender and sexual diversity in schools. Yet, colloquial ideas about “professionalism,” others’ apparent discomfort discussing gender nonconformity, and transphobic sentiment can lead candidates to perceive that transness and gender nonconformity in teacher candidates is unwelcome. Some candidates experience similar subtle exclusions related to race and ability, leading to particular concerns among multiply marginalized candidates about anticipating and navigating others’ perceptions of themselves as prospective teachers. In response, candidates in this study came to consider being a trans and/or gender nonconforming candidate in a teacher education program as increasing one’s vulnerability to harm and as involving additional labour to negotiate or respond. At the same time, many participants described moments of joy and connection that were enabled by their presence as trans and/or gender nonconforming people in schools or teacher education programs, leading the group to also conceptualize being trans and/or gender nonconforming as a potential asset in the classroom. I suggest that our meetings created space to collectively develop and try out new ways of conceptualizing ourselves as trans and gender nonconforming educators and to consider how teacher education might be otherwise, even as our work together was also riddled with anxieties, disappointments, and silences.
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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 | 0.024 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.070 | 0.028 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| 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 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".