Electric Corset: an approach to wearables innovation
Bibliographic record
Abstract
One criticism of electronic textiles and wearable technology is that instead of being integrated into the modern wardrobe, the electronic garment is perceived as the ‘other’, as an ‘unusual’ item within the wardrobe. Contemporary fashion is a field of play in which individuals constantly manage personal expressions of social belonging and transgression, at the same time as it closes down the potential for new forms as a result of increasingly fast fashion supply chains. The Electric Corset project proposes that the uptake of wearables is compromised when development is based on modern categories of dress/dressing, and proposes that designers look to obsolete and ‘in-between’ items of dress to rethink the foundations of wearables development. In collaboration with Nottingham Museums and Galleries Costume and Textiles Collection, we have reproduced a small selection of such items, and recast them as ‘sacrificial’ toiles to provide a non-precious basis for embodied experimentation. The paper describes some of the barriers to innovation in wearable technologies, and frames our approach through the twin concepts of deconstruction and reconstruction in fashion theory. It reports on our experiences of embodied responses to the toiles within the making process, and presents early findings from a pilot study using improvisation.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".