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Record W1998695157 · doi:10.1145/1520340.1520378

Designing CALLY,

2009· article· en· W1998695157 on OpenAlexaff
Ji-Dong Yim, Chris Shaw

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBrainstormingHuman–computer interactionComputer scienceImplementationIdeationRobotProcess (computing)Participatory designPhoneMultimediaSoftware engineeringEngineeringArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

This proposal describes the early phase of our design process developing a robot cell-phone named CALLY, with which we are exploring the roles of facial and gestural expressions of robotic products in human computer interaction. We introduce non-verbal anthropomorphic affect features as media for building emotional intimacy between a user and a product. Also, two social robot application ideas generated from brainstorming and initial participatory design workshop are presented with their usage scenarios and implementations. We learned from the pilot test that the prototyping and bodystorming ideation technique enabled participants to more actively take part in generating new ideas when designing robotic products.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0490.014

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 head0.021
GPT teacher head0.279
Teacher spread0.258 · 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 designNot applicable
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

Citations8
Published2009
Admission routes1
Has abstractyes

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