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Electric Corset: an approach to wearables innovation

2019· book-chapter· en· W2746361365 on OpenAlexfundno aff

Bibliographic record

VenueDiscovery Research Portal (University of Dundee) · 2019
Typebook-chapter
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsnot available
FundersTrent UniversityNottingham Trent University
KeywordsWearable computerEmbodied cognitionDeconstruction (building)ImprovisationField (mathematics)Wearable technologyProcess (computing)AestheticsHuman–computer interactionComputer scienceEngineeringArtVisual artsArtificial intelligence

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.941
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.126
GPT teacher head0.278
Teacher spread0.152 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations2
Published2019
Admission routes1
Has abstractyes

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