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Record W3040088910 · doi:10.1145/3357236.3395532

JeL

2020· article· en· W3040088910 on OpenAlexaff
Ekaterina R. Stepanova, John Desnoyers-Stewart, Philippe Pasquier, Bernhard E. Riecke

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFeelingSynchronization (alternating current)Connection (principal bundle)Computer scienceProcess (computing)Human–computer interactionReflection (computer programming)Virtual realityBreathingTelecommunicationsPsychologyEngineeringSocial psychology

Abstract

fetched live from OpenAlex

Bio-responsive immersive Virtual Reality can transform our interactions to bring awareness to our physiological rhythms fostering connection with our bodies, each other and nature. JeL is an immersive installation that aims to foster a feeling of connection through the process of breathing synchronization. Two immersants synchronize their breathing to fuel the growth of a coral-like structure that, together with the interactions of others, populates an initially empty coral reef. JeL is designed to support an intimate connection between users and with nature, sending a message about our collective capacity to care for the environment. JeL is an installation and research platform for exploring breathing synchronization and its effect on the feeling of connection. It was well received at a digital art festival where participants were able to relax and synchronize using the installation. Reflection on our design process and observations provides insights for the development of systems that promote connection.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.556
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.4440.186

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 head0.050
GPT teacher head0.252
Teacher spread0.202 · 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.

Study designNot applicable
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

Citations65
Published2020
Admission routes1
Has abstractyes

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