Enclothed Knowledge: The Fashion Show as a Method of Dissemination in Arts-Informed Research
Bibliographic record
Abstract
In this article, I investigate the processes, benefits, and dilemmas of producing a fashion show as a method of dissemination in arts-informed qualitative research. I examine a project that used a fashion show to analyze and represent interview findings about men’s understandings and performances of masculinities. Fashion shows facilitate the dissemination of new qualitative data — what I coin “enclothed knowledge” — that is embodied and inaccessible through static or verbal descriptions. Fashion shows also enable participants to shape knowledge circulation and allow researchers to engage diverse audiences. Despite these benefits, researchers have to be mindful of ethical dilemmas that occur from the absence of anonymity inherent in public performances; therefore, I suggest strategies to mitigate these threats to research ethics. Ultimately, I argue that fashion shows advance social justice because the platform can transform narrow, stereotypical understandings of marginalized identities.
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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 | 0.105 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".