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Record W2023113668 · doi:10.1145/2559206.2581360

The laughing dress

2014· article· en· W2023113668 on OpenAlexafffund
Sunmin Lee, Wing Yi Chung, Emily Ip, Thecla Schiphorst

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsWearable computerLaughterAffordanceHuman–computer interactionContext (archaeology)MirroringWearable technologyInteraction designProsocial behaviorComputer scienceEmpathyEmotional contagionPsychologySocial psychology

Abstract

fetched live from OpenAlex

Our research introduces a responsive wearable design that explores laughter as an emotional contagion between strangers in a public space. We investigate how interactive wearable technology can support expression and communication through laughter as prosocial behaviour within the context of a public art installation. We base our design on psychological research that explores emotional contagions and psychophysiological mirroring. While most of this research has focused primarily on internal biological data, there is little design research that has investigated the phenomenon of emotional contagion in a social space utilizing wearable technology, particularly within HCI. We conducted a mixed methods pilot study, which has indicated that wearable technology can create affordances for emotional mimicry by testing the effectiveness of visual and auditory cues embedded within the wearable design. Our research provides insight to help evaluate effective design strategies in wearable interaction that can ameliorate positive social interaction between people.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score0.280

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 communication0.0000.000
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 head0.007
GPT teacher head0.240
Teacher spread0.233 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations10
Published2014
Admission routes2
Has abstractyes

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