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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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 teacher head, 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".