Queer, Trans, and/or Nonbinary French as a Second Language (FSL) Teachers’ Embodiment of Inclusivity in Their Teaching Practice
Bibliographic record
Abstract
Increasingly, scholars are attending to questions of identity and power in French as a second language (FSL) education. An underdeveloped area of research is the experience of queer, trans, and nonbinary FSL teachers in Canada. Understanding how marginalized teachers navigate building inclusive and equitable learning spaces is the focus of this study. To this end, this study used narrative inquiry and photo elicitation methods to understand how—if at all—participants embody inclusivity in their classroom practices. Four themes emerged from this study: (1) (in)visibility of queerness, (2) performing a balancing act, (3) urgency to disrupt, and (4) navigating the teaching of a gendered language. These findings suggest that while participants in this study strive to build inclusive spaces for themselves and their students, external factors, such as fear of opposition and being reprimanded, abound. These findings offer insights into discursive moves to facilitate a meaningfully queered and inclusive FSL learning space, and contributes to the growing body of queer applied linguistics by revealing how queer teachers’ embodied practices can reshape inclusivity in FSL education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".