Empowering women wearing plus-size clothing through co-design
Bibliographic record
Abstract
The retail landscape includes a vast array of clothing choices, yet style options remain extremely limited for Canadian women in the plus-size category (sizes 14W–40W). Our study empowered women who wear size 20+ by bringing them into the conversation about plus-size apparel design and development. Few studies have identified clothing solutions utilized by plus-size women or how clothing impacts their feelings about themselves, and there is no research on the clothing needs of women in the upper plus-size range. We recruited participants through Facebook posts to plus-size communities and clothing swap groups located in a major Canadian city. Our research design had a human-centred focus and included co-design methods. Activities included body mapping, body scanning and co-design activities with sixteen women in a full-day workshop to unpack their ideas about plus-size clothing in a body-positive space to foster confidence, strength and autonomy. Body maps allowed our participants to embrace creativity as a tool to communicate meaning in an empowering way. Body scanning provided a quick way to electronically capture body shape and size through circumferential measurements. Co-design activities included drawing and writing. Proposed clothing designs were drawn on body templates derived from participant’s personal body scans. Participants elaborated on their clothing ideas by completing a needs and features chart to share perceived problems and propose solutions. Emergent themes included participants’ ideas about meaning and empowerment, proposed clothing designs, detailed information regarding clothing fit and selection challenges, as well as their feelings about the co-design process. Consultation with people, using co-design methods is a way to reveal fashion gaps and an opportunity to improve customer satisfaction and increase sales and thus is important to designers and retailers specializing in the plus-size market.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| 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".