Multi-sensory methods: Toward a crip methodology in fashion studies
Bibliographic record
Abstract
Cripistemologies are ways of knowing that emerge from the embodied experiences of disabled bodyminds, while multi-sensory methods value knowledge produced through haptic, visual, auditory and other sensory forms. Drawing on these approaches, this article proposes a crip methodology for fashion studies by exploring the value of combining research methods to honour the knowledge of disabled people and their sensory engagement in the world. We reflect on the four phases of our project, ‘Cripping Masculinity’, and its entangled methodologies and methods. The project engaged 50 disabled, D/deaf and neurodivergent men and masculine people – who we refer to as collaborators – to examine how they produced masculinity and disability through their engagement with fashion and dress. Collaborators took part in wardrobe studies where they shared their experiences with their clothing; fashion hacking where they worked with design students to deconstruct and remake one of their existing garments to better support their bodyminds, and a fashion exhibition and fashion show where they co-produced events to disseminate their dress experiences and hacked clothing. Our analysis demonstrates that combining multi-sensory methods in one project recognizes the different ways that people inhabit the world as sources for generating and disseminating knowledge about the relationship between disability and fashion. While a crip methodology poses challenges for producing research in legible scholarly forms, it also urges fashion studies scholars to intervene into academic ableism through inclusive and expansive research methodologies and methods.
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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 | 0.171 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.007 | 0.048 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".