We Are Not Alone: Queer Teachers’ Navigating Personal and Professional Identity; A Qualitative Study
Bibliographic record
Abstract
This qualitative study explored how Queer teachers navigate their personal and professional identities. The research questions sought to understand how Queer teachers balance their Queer identity in their personal and professional lives and how they find support for their identities in their workplace. Goodson’s study of teachers' lives and Alsup’s on teacher identity negotiation were used as a theoretical lens to bring greater attention to a neglected subject and situation: the lack of belonging that (as several studies show) Queer teachers tend to feel in school. The research design combined self-study with a focus group discussion. The autobiographical self-study, inspired by Grace's concept of writing to the Queer self, allowed the researcher/teacher to reflect on their own experiences as an openly Queer teacher navigating their identities. The focus group participants were openly Queer individuals who had been working as teachers for 3-5 years in the Greater Montreal area. The findings revealed that Queer teachers feel more supported with respect to their identities in environments that provide professional development, and a supportive community and resources for students. The research allowed the teacher-researcher to reflect on their own experiences as well for Queer teachers to reflect together—an all too rare but necessary opportunity that shows promise for future work in research and practice. The focus group format generated momentum for change, as teachers shared ideas for improving the working conditions of Queer teachers in the Quebec education system. The thesis concludes with recommendations for educational leaders and policy makers on how to best support Queer teachers
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.018 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".