Expressive Clothing: Understanding Hobbyist-Sewers' Visions for Self-Expression Through Clothing
Bibliographic record
Abstract
Researchers have found that hobbyist-sewers seek to create new or adapted clothing designs that foster self-expression through communicating ideas, opinions and emotions. Although existing sewing technologies enable designing new patterns, they focus only on the technical aspects of pattern drafting and not on how information can be expressed. To address this gap, we conducted a qualitative diary study with 12 hobbyist-sewers to better understand how they envision creating expressive clothing. From our analysis of the 24 expressive clothing sketches participants created and participant interviews, we identified i) five distinctive multifaceted approaches participants used for self-expression; and ii) four challenges participants identified from their design process. Informed by these insights, we present a set of implications for the design of future technologies that can better support hobbyist-sewers in designing and creating expressive clothing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".