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Record W4366729482 · doi:10.1145/3544549.3583896

Demonstrating Virtual Teamwork with Synchrobots: A Robot-Mediated Approach to Improving Connectedness

2023· article· en· W4366729482 on OpenAlexaff
Yuna Watanabe, Xi Laura Cang, Rúbia Reis Guerra, Devyani Mclaren, Preeti Vyas, Jun Rekimoto, Karon E. MacLean

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsUniversity of British Columbia
FundersUniversity of Tokyo
KeywordsTeamworkComputer scienceHuman–computer interactionTask (project management)InteractivitySocial connectednessCreativityWearable computerAsynchronous communicationRobotLaptopZoomSet (abstract data type)CuriosityMultimediaApplied psychologyPsychologyArtificial intelligenceSocial psychologyEngineeringComputer network

Abstract

fetched live from OpenAlex

The increased prevalence of online collaborative work, through necessity or preference, is accompanied by measurable drops in satisfaction, creativity and energy, often termed “zoom fatigue.” As loss of physical co-presence and associated nonverbal communication are identified as contributors, we introduce Synchrobots – robots designed to channel human biophysiology for group connectedness. We propose an Interactivity demo wherein two participants perform an online problem-solving task while wearing physiological sensors and holding a Synchrobot as it physically renders a translation of their partner’s heartrate. The setup involves two stations, each with a laptop running Zoom, a set of wearable sensors recording heart rate, respiratory rate, and electrodermal activity, and a Synchrobot. After the problem-solving task, we will invite participants to reflect on how connected they felt with each other as well as their satisfaction with the collaboration quality. Participants may consent to release this data for later inclusion as part of a study.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.241
Teacher spread0.222 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2023
Admission routes1
Has abstractyes

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