Voices of Experience: Queer Language Teachers’ Advice for New Second Language Educators
Bibliographic record
Abstract
This study presents the narratives of 12 queer-identifying second language (SL) teacher experiences in Canada as they offer advice to their queer pre-service and early-career peers about the realities and complexities of teaching a SL. Using narrative inquiry with three rounds of semi-structured interviews, the data reveal several themes about the experiences of queer SL teachers in their initial teacher education (ITE) program: SL teachers lack support, resources, and mentorship in ITE and in beginning practice around navigating the intersections of their identities, SL pedagogy, community, and policy. Implications for SL ITE programs to enhance inclusivity for queer pre-service teachers are offered, particularly in relation to increasing inclusive language teaching strategies, disrupting anti-queer hate in schools, and the need for mentorship opportunities for queer SL 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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.035 | 0.020 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".