Run Like a Girl? That's So Gay: Exploring Homophobic and Sexist Language among Grade 7 and 8 Students in the Toronto District School Board
Bibliographic record
Abstract
Homophobic name-calling abounds in schools, especially at the high school level. Driving much of this research is the negative impact of anti-gay language and epithets on lesbian, gay, bisexual and trans (LGBT) identified students, students with LGBT parents and guardians, and students overall. Studies in the US, UK, and Canada report regular use of phrases such as ‘that’s so gay’, ‘faggot’, and the word “gay” itself to denote something or someone with little to no value. However, a dearth of research looking at the prevalence of homophobic language at the middle school level exists. Furthermore, there is even less research looking at the relationship between homophobic and sexist language use. Using a mixed methods explanatory sequential design, this three-paper dissertation explored homophobic and sexist language use in middle school, specifically investigating, among other things, how often and under what conditions grade seven and eight students use these types of language. A stratified random sample of middle school students (n=488) completed a survey that included the Homophobic Content Agent Target Scale (HCAT). The newly developed Sexist Content Agent Target Scale (S-CAT) explored sexist language. Students reported their frequency of homophobic and sexist language use in five relationship domains including friends, strangers, and antagonists. As well, five focus groups explored more deeply students’ perceptions and understandings of homophobic and sexist language use at school. A number of important findings were produced including the prevalence of homophobic name-calling prior to high schools as well as the strong association between homophobic and sexist language use. These empirical findings join the literature that has long conceptually linked sexism and homophobia, whereby their interlocking nature manifests in name-calling experiences of middle school students. Students (boys in particular) as either agents or targets of sexist language had increased likelihood of being agents or targets of homophobic language. Given the often contentious nature of anti-homophobia education, the hesitancy of school staff to intervene regularly (even in the presence of this language), the findings presented here suggest anti-sexist education as another means to address homophobic language, thereby contributing to creating and maintaining more positive learning environment for all students.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".