Bibliographic record
Abstract
The purpose of this essay is to demonstrate that the mechanisms of power around classifications of gender and sexuality are not always top-down or bottom-up. Instead, the weight of social discipline among members of sexual subcultures themselves helps to create these classifications, often reflecting the nomenclature of subjects and desires within sexual subcultures in a complex relationship to a dominant culture. Critically examining two benchmarks in the development of sexual nomenclature within queer subcultures, this paper finds its evidence in George Chauncey's little known analysis (1985) of a navy investigation of male homosexuality at the Newport Naval Training Station during the World War I era and in contemporary folksonomic classifications of representations of queer desire within Xtube, a database of online pornography. Social discipline within these sexual subcultures occurs in the stabilization of nomenclature through socialization and through members' overt intervention into each others' self-understanding. Both the Newport and Xtube evidence also reveals a complex social and cultural structure among members of sexual subcultures by drawing our attention to the particularity of various modes of sexual being and the relationship between those modes and particular configurations of sexual identity. In the process, this paper allows us to reassess, first, a presupposition of folksonomies as free of discipline allowing for their emancipatory potential and, second, the prevailing binary understandings of authority in the development of sexual nomenclatures and classifications as either top-down or bottom-up.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".