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Record W2089635306 · doi:10.1386/padm.2.2.171_1

Breath, skin and clothing: Using wearable technologies as an interface into ourselves

2006· article· en· W2089635306 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Performance Arts and Digital Media · 2006
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsClothingWearable computerInterface (matter)Human–computer interactionWearable technologyComputer scienceAestheticsArtHistoryOperating system

Abstract

fetched live from OpenAlex

AbstractThere is a common ground that exists between the first person methodologies of performance practice and the technology research of Human—Computer Interaction (HCI). Exploring this common ground, this essay describes movement research based in performance and somatics and then applied to the design of digital networked interfaces for wearable technologies. The research is based on a body of knowledge practices from performance/somatics that operate ‘from the inside out’, using the experience of the moving body to construct knowledge. Within both performance practice and HCI, there is a need to construct models of the user's experience. One of the key questions this paper asks is: How can we bridge specific domain knowledge within performance practice to transform design strategies for our new technologies? The first section provides a theoretical context for bridging embodied practices from performance to HCI, and looks at (1) how performance methodologies can be used as a model for experience, (2)...

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.808
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.005
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.281
Teacher spread0.267 · 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