Hand Spinning E-textile Yarns: Understanding the Craft Practices of Hand Spinners and Workshop Explorations with E-textile Fibers and Materials
Bibliographic record
Abstract
The ‘material turn’ in Human Computer Interaction (HCI) is increasingly drawing attention to the computational affordances of materials and how we can craft with them. In this paper, we explore opportunities for combining the maker cultures of hand spinning with e-textile crafting. In our first study, we interviewed 32 hand spinners on their practices to better understand their motivations for spinning their own yarns and the techniques they use to do so. In our second study, we conducted workshops with 6 spinners at a local spinning guild, where participants worked with the conductive fibers and spun e-textile yarns. After the workshops, we conducted follow-up interviews with each participant to understand the opportunities and tensions of hand spinning e-textile yarns. Our findings show how spinners can blend local materials with conductive ones to develop their own custom interactive textiles, and the mismatch between how these fibers are sold and what information spinners require to inform their design decisions. Through these results, we hope to empower makers and inspire the design community to develop tools to support these DIY practices.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".