Aesthetic as genetic: The epistemological violence of gaydar research
Bibliographic record
Abstract
In recent years, “gaydar” has come under increasing scientific scrutiny. Gaydar researchers have found that we can accurately judge sexual orientation at better than chance levels from various nonverbal cues. Why they could find what they did is typically chalked up to gender inverted phenotypic variations in craniofacial structure that distinguish homosexuals. This interpretation of gaydar data (the “hegemonic interpretation”) maintains a construction of homosexuality as both a “natural kind” and an “entitative” category. As a result, culturally and historically contingent markers of homosexuality are naturalized under the guise of gaydar. Of significant relevance to this article’s critique of gaydar research is that the hegemonic interpretation is presented as politically advantageous for LGB people by its authors, an undertheorized assumption that risks sanctioning an epistemological violence with unfortunate, demobilizing sociopolitical consequences. This critique is contextualized within current debates regarding intimate/sexual citizenship and advocates, instead, for a queer political ethic that considers such cultural erasure to be politically untenable.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.110 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".