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Record W2899336012 · doi:10.1145/3274460

Shopping Over Distance through a Telepresence Robot

2018· article· en· W2899336012 on OpenAlexaff
Lillian Yang, Brennan Jones, Carman Neustaedter, Samarth Singhal

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2018
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsUniversity of CalgarySimon Fraser University
Fundersnot available
KeywordsRobotHuman–computer interactionTeleroboticsComputer scienceMobile robotMultimediaArtificial intelligence

Abstract

fetched live from OpenAlex

Computer mediated-communication tools (CMC) support loved ones in maintaining connections with one another over distance, yet it can be difficult to do activities together. We studied the use of telepresence robots for supporting distance-separated loved ones in engaging in the joint activity of shopping over distance. One partner shopped in person while the other used either a telepresence robot or a tablet from a remote location. Compared to the tablet group, we found that when partners communicated through a telepresence robot, the remote partner's personality and presence were expressed through the movements and physicality of the medium. However, the use of the telepresence robot introduced tension between partners regarding responsibility, dependency, and contribution to the act of shopping. These results demonstrate the benefits of a mobile embodiment for remote partners, as well as the need for greater physical capabilities to support both physical connection and remote contribution to leisure activities.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.114
GPT teacher head0.427
Teacher spread0.313 · 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 designObservational
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

Citations61
Published2018
Admission routes1
Has abstractyes

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