MétaCan
Menu
Back to cohort
Record W2610623528

Teachers' Experiences of Including Gender and Sexual Diversity Topics in French as a Second Language in Classrooms in Ontario

2017· article· en· W2610623528 on OpenAlexaboutno aff
Daniel E. Couture

Bibliographic record

VenueTSpace (University of Toronto) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender Studies in Language
Canadian institutionsnot available
Fundersnot available
KeywordsDiversity (politics)Gender diversityPsychologyGender studiesSociologyLinguisticsPedagogyAnthropologyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.049
GPT teacher head0.310
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueTSpace (University of Toronto)Same topicGender Studies in LanguageFrench-language works237,207