A Fashion Studies Manifesto: Toward an (Inter)disciplinary Field
Bibliographic record
Abstract
We are two scholars who locate ourselves within Fashion Studies and outside of it. In this paper, we draw from our professional experiences and research programs to argue that Fashion Studies should be both a stand-alone and a cross-disciplinary field. Fashion provides a lens to understand the social, visual, and material worlds, while Fashion Studies fosters community among scholars who are deeply “invested” in the study of dress, adornment, and the body. First, framing our discussion through autoethnographic and embodied lenses allows us to how examine how we came into Fashion Studies. Our academic backgrounds are outside of Fashion Studies, but these routes led us to become faculty within a fashion department and inspired our work in building a diverse Fashion Studies community. Second, we explore our research programs as examples of how scholars in our field have to strategically position themselves. The federal grant system in Canada, where we live and work, does not recognize Fashion Studies. As such, we have made the case that fashion is a lens to advance new knowledge in History and Sociology, established disciplines recognized by granting agencies. We discuss how we made this case in our applications, discussing how Fashion Studies’ frameworks, methodologies, and tools of mobilization have allowed us to contribute to a range of disciplines. Finally, we discuss the need for Fashion Studies to welcome scholars who recognize diverse ways of knowing in order to decolonize, queer, and crip the field. The global pandemic, the Black Lives Matter movement, and the climate change crisis have alerted us to the urgency of the need for care, communication, and community. We discuss how we have supported graduate students from diverse intersections as Fashion Studies scholars and those from established fields. We ask how we can continue to create spaces for questioning and reflection to ensure that the powerful voices of the next generation of scholars and creatives in the field we love will be heard loud and clear.
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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 | 0.036 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.019 | 0.040 |
| Scholarly communication | 0.031 | 0.016 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.004 | 0.010 |
| 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".