Re-Making Clothing, Re-Making Worlds: On Crip Fashion Hacking
Bibliographic record
Abstract
This article explores how Disabled people’s fashion hacking practices re-make worlds by expanding fashion design processes, fostering relationships, and welcoming-in desire for Disability. We share research from the second phase of our project, Cripping Masculinity, where we developed fashion hacking workshops with D/disabled, D/deaf and Mad men and masculine non-binary people. In these workshops, participants worked in collaboration with fashion researchers and students to alter, embellish, and recreate their existing garments to support their physical, emotional, and spiritual needs. We explore how our workshops heeded the principles of Disability Justice by centring flexibility of time, collective access, interdependence, and desire for intersectional Disabled embodiments. By exploring the relationships formed and clothing made in these workshops, we articulate a framework for crip fashion hacking that reclaims design from the values of the market-driven fashion industry and towards the principles of Disability Justice. This article is written as a dialogue between members of the research team, the conversational style highlights our relationship-making process and praxis. We invite educators, designers, and/or researchers to draw upon crip fashion hacking to re-make worlds by desiring with and for communities who are marginalized by dominant systems.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.036 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".