Gender Diversity and Sexuality in English Language Education
Bibliographic record
Abstract
<JATS1:p>Shortlisted for the 2023 British Association for Applied Linguistics (BAAL) Book Prize</JATS1:p> <JATS1:p>This book combines teaching-informed research studies and research-informed teaching accounts which explore English language education that engages with gender and sexual diversity. Informed by critical theories, critical literacy, post-structuralism, queer theory, and indigeneity/(de)coloniality, the critical perspectives in this volume consider gender and sexuality as dimensions of human life and aim to promote sexual, gender, emotional and relational wellbeing together with the construction of cultural horizons and citizenship. The chapters are organised around three interdependent areas of inquiry: 1) how educators design pedagogies and curriculums around gender diversity, 2) how students and teachers navigate issues of gender diversity in practice, and 3) how issues of gender diversity are and aren't addressed in the materials for teaching and learning English.</JATS1:p> <JATS1:p>The contributors are all teacher educators-researchers and therefore have vast experience in enacting, implementing, designing, and examining the field of English language teacher education from/for the classroom with a gender perspective in diverse settings, with chapters come from Argentina, Bangladesh, Canada, Germany, Norway, Poland, Saudi Arabia, South Africa, Spain, Taiwan, Turkey, the UK and Uruguay.</JATS1:p>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".