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Record W2898791345 · doi:10.1145/3274459

Our House

2018· article· en· W2898791345 on OpenAlexaff
Lillian Yang, Carman Neustaedter

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2018
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsInteractivityHelpfulnessFeelingAutonomyRobotVariety (cybernetics)Internet privacyInterpersonal communicationPsychologyHuman–computer interactionSocial psychologyComputer scienceMultimedia

Abstract

fetched live from OpenAlex

Many couples live in long distance arrangements due to a variety of reasons from work opportunities to family obligations. However, current computer-mediated communication tools are not designed to support the entire range of communication behaviors that couples engage in during daily life. Our research explored how telepresence robots might fit into the array of tools that long distance couples use to communicate, given the factors of mobility and physicality that could support acts of interactivity and autonomy between partners. We found that telepresence robot communication facilitated interactions in five areas: participation in mundane everyday routines, feelings of sharing a home, connection with one's partner's family and friends, increased helpfulness, and the enjoyment of quiet companionship. However, telepresence robots also presented challenges related to privacy and asymmetry, as well as continued deficiencies in the level of interactivity. In response to these findings, we discuss the design implications for telepresence robots.

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)0.002
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: none
Teacher disagreement score0.745
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0040.002
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.7450.408

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 head0.137
GPT teacher head0.432
Teacher spread0.295 · 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

Citations53
Published2018
Admission routes1
Has abstractyes

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