Teachers' Experiences of Including Gender and Sexual Diversity Topics in French as a Second Language in Classrooms in Ontario
Bibliographic record
Abstract
The purpose of this study was to explore the experiences of French as a second language (FSL) teachers in Ontario who are including topics related to gender and sexual diversity in their classrooms. Semi-structured interviews with two Ontario FSL teachers who reported including these topics in their lessons provided significant insights into this unique teaching experience. First, the normalization of queer people and queer issues was the primary objective of teachers when including these topics in their French as a second language classrooms. Moreover, teachers perceived that the introduction of these topics in their classrooms presented a benefit to their students’ language learning. Nevertheless, teachers reported it to be difficult to locate French language resources to support their inclusion of these topics in their lessons and reported both negative and positive responses to their work from various stakeholders in their school communities. Findings suggest that the normalization of queer people in Ontario schools has still not been achieved and that the work of teachers who include gender and sexual diversity topics in their classrooms is still necessary. Recommendations are offered for teachers and administrators regarding equity in extracurricular activities, professional development and accountability with respect to existing policy.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".