Catchalls and Conundrums: Theorizing “Sexual Minority” in Social, Cultural, and Political Contexts
Bibliographic record
Abstract
The term “sexual minority” functions in social, cultural, and political contexts as a catchall for minority sexuality categories. Yet, apart from serving as an umbrella term, its uses are contradictory. On the one hand, the term emphasizes “sexuality,” which serves the purposes of religious fundamentalist and political groups that demonize minority sexualities to the exclusion of identity, background or family status. On the other hand, the term can be useful for readers and researchers in sexuality studies to become more globally aware of, and to reconceptualize, sexuality outside of tightly contained LGBT boxes. Such a contradiction has implications for education practice and policy. We suggest, for instance, using the term cautiously when describing same-sex sexualities because, as an umbrella term, it can homogenize people who represent a highly diverse spectrum of racialized categories, class backgrounds, genders, sexualities, and other social markers of difference. As a pedagogical heuristic device, the term is useful in delineating the differences between queer and sexual minority pedagogies when deciding upon the approach that will best draw an audience into the discussion. Our overall goal through this critical exploration is to support new understandings and insights of sexual diversity in ways that effectively challenge heterosexism and homophobia.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.014 | 0.102 |
| Scholarly communication | 0.012 | 0.027 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".