The Right Fit: A Clothing Needs Assessment of Women with Plus-size Bodies (20+)
Bibliographic record
Abstract
Although access to ready-to-wear fashion has improved within the plus-size market, women wearing size 16 & up still lack access to fashionable clothing; those sized in the upper half of this range are further marginalized. The clothing experiences of women (n=16) wearing size 20+ were collected during a full-day workshop. Co-design methodologies were used to engage these consumers to identify their clothing needs and aspirations. Each participant had a 3D body scan; the resulting personalized body outline was printed in black and white on tabloid size paper. Participants completed a chart outlining their clothing needs, suggested clothing features as possible solutions, and illustrated their design ideas for an outfit or a specific garment on their personalized body outline. Results were categorized according to wardrobe issues, specific problematic garment areas and body parts that participants were self-conscious about in clothing. Findings are important to apparel designers and educators, product developers and retailers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".