Queering English language teaching materials: censorship and intersectional approaches
Bibliographic record
Abstract
As researchers such as Gray (2013) and Paiz (2015) have established, textbooks within the industry of English Language Teaching (ELT) have historically done a poor job of incorporating queer content. The industry of ELT textbooks is lucrative with a global reach, so the impact the books have is very significant. In the name of profits, it is common to see the most conservative version of a textbook used for all markets, which means that LGBTQ representation is virtually invisible (Gray, 2010). It may be done in the name of cultural sensitivity, but as Kimberlé Crenshaw (1991) says in her landmark work on intersectionality, “ignoring differences within groups frequently contributes to tension among groups.” At the same time, LGBTQ issues in ELT have received growing attention, and researchers have explored the best practices to incorporate queer content. Judith Butler’s (2006) inquiry-based approach, as adapted by Nelson (2002) to challenging the ways gender norms are dictated by society and imposed through language has been particularly influential. Several teacher-researchers have built on this and taken into account the political implications of ELT with “intersectional approaches” using texts written by queer people from outside Kachru’s (1986) “Inner Circle.” Examples of this would be O’Mochain (2006), who had students in Japan read first person accounts of queer Japanese people, and Paiz and Zhu (2018), which recounts Paiz’s use of Sri Lankan-Canadian writer Selvadurai’s (1994) novel Funny Boy as a way of introducing queer themes to a class of university students in China. In both cases, this approach yielded positive results, and it is from this that there is a framework for researching and assessing the extent to which the most recent textbooks and online materials have changed (if at all) regarding gender and sexuality, and if it is done successfully. Gray and Cooke (2018) call for more study connecting queer intersectionality and linguistics, looking at people’s lives as being complex and dynamic as well as shaped by multiple different forces of gender, race, economics, and nationality. For this master’s thesis, I used this framework to analyze some of the most recently published ELT textbooks to determine whether they have improved in terms of LGBTQ representation, and suggested ways that the materials could be improved. I also looked at online materials, including lessons published several years ago, as well as recently as all are currently available, and found that there are more attempts to address LGBTQ topics (primarily just lesbian and gay) than there are in textbooks but that the approaches are often problematic. I suggested ways that they could be improved, and also provided examples from film, literature, and online resources that could be adapted into ELT texts
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".