Bibliographic record
Abstract
SUMMARY Anti-homophobia education is rarely included in the anti-bias curriculum of Education faculties, a grave omission since education graduates will teach in a homophobic school system that oppresses gay and lesbian students. This article draws on my experience in using a range of anti-homophobia strategies to confront homophobia among religious students in critical education courses where the principle of respecting each and every child is foundational. I argue that strategies designed to produce empathy sometimes fail because of the extreme importance attached to homophobia in the religious discourses that structure the identities of these students. At such times we should shift our pedagogical efforts to confront the ethical conflicts between homophobia and the principle of respect. I describe how I focus on the discursive production of both the forms and limits of personal identity, feelings, and beliefs to handle the confrontation productively. Although confronting homophobia sometimes involves hearing hurtful speech, it usefully problematizes the ethical status of homophobic students who are otherwise committed to classroom democracy, often provoking them to adopt less oppressive behaviors. It also usefully exposes the existence of homophobia for other students who might have underestimated it. Both groups end up better prepared to fight homophobia in their work as teachers.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".