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Record W2115306233 · doi:10.1080/14626260701743200

Embodied imagination: a hybrid method of designing for intimacy

2007· article· en· W2115306233 on OpenAlexaff
Lone Koefoed Hansen, Susan Kozel

Bibliographic record

VenueDigital Creativity · 2007
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSituatedEmbodied cognitionWearable computerComputer scienceHuman–computer interactionExperiential learningDomain (mathematical analysis)Process (computing)Wearable technologyExploratory researchSituated learningMultimediaSociologyArtificial intelligence

Abstract

fetched live from OpenAlex

Situated in the domain of research into mobile, wireless, networked and wearable computing, this exploratory paperintroduces the embodied imagination method and explains how it can contribute to the design process by creating an elastic space of performance that incorporates daily life and personal imagination into the design process. It is based on a study called Placebo Sleeves which was an experiential design phase of a larger project in wearable computing called whisper[s]. The innovation offered by this research is twofold: an integration of previously distinct methodologies, and an interdisciplinary theoretical framework relevant to the design of devices for affective, networked communication. The methodologies are shaped both by user experience models and by performance practices. We also articulate a domain of public dreaming, located at the conjunction of the private, public and secret within human existence, and suggest that shared use of mobile technologies has the potential to be situated there.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.019
Scholarly communication0.0080.009
Open science0.0020.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.029
GPT teacher head0.348
Teacher spread0.318 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations46
Published2007
Admission routes1
Has abstractyes

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