Bibliographic record
Abstract
There are few sartorial ensembles as heavily signified as masculine as a suit. This article focuses on the suit within queer fashion digital cultures and spaces to explore how butch of colour digital fashion suits up to offer us different ways to think about masculinity. Intervening in the erasure of women of colour in histories of fashion – including menswear – and histories of sexuality – butch, dapper, tomboy, dandy – I argue that butch digital fashion works as a site and composition of flesh, fabric and feeling that reworks masculinity as a project of embodiment. I look at three interwoven dimensions of butch digital fashion – aesthetic process, texture as feeling and spatial imaginations – attending to the themes of fantasy, desire and pleasure. I situate butch within and between fashion studies and media studies to offer butch as a relation, practice, orientation and site of embodiment to think about being and becoming, about the body politics of space and feeling, as matters of race, sexuality, class and gender – and the materialities of race, sexuality, class and gender, mediated by the global, and as the global by the digital. Digital butch fashion is, at once, a visual culture, a creative visual space, a resource of queer fantasy and an aesthetic process. It is messy, tense and fraught with the politics of race, colonialism and class, yet at the same time, dense with possibility, pleasure and eroticism. Butch of colour fashion offers frames and forms for rethinking and remaking masculinity as a sign of a body, as a category of personhood, as a set of practices and feelings made coherent by processes of embodiment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".