Custom Clothing Technology: Diffusion of Luxury Practices in Fashion
Bibliographic record
Abstract
The common understanding of the fashion industry is that it is rapidly changing and constantly on the cutting edge of what is new. Yet in reality the fashion industry does not adopt new practices or change its ways of doing business quite so easily. This article examines the successes and failures of 3D scanning as a new tool in the fashion industry. Through the analysis of three case studies it becomes clear that new technology is not an automatic guarantor of innovation or success. Analyzing the motivations behind the introduction of 3D scanning for made-to-measure clothing products is important to understanding where technology and the fashion practitioner do not necessarily communicate properly. Whereas 3D scanning promises to make made-to-measure clothing an easy and accessible service, made-to-measure and other custom clothing businesses are based upon traditional notions of luxury and craftsmanship. It is apparent through first-person interviews and observations that the current dichotomy between technology and craftsmanship has not been resolved. Creators of fashion-based technologies need to be working in tandem with traditional fashion practitioners, whose expertise is required if new technology is to reinvent the centuries-old processes of clothing production for the better. 3D scanners that have been introduced to date have yet to meet their full potential because they lack the nuanced understanding of the human body that comes from traditional clothes-making training and expertise. Researching the present status of this technology’s integration within fashion is important in understanding how digital technology is best included in the design, production, and sale of clothing products more broadly.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".