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Record W2623717247 · doi:10.1145/3064663.3064722

Critiquing Physical Prototypes for a Remote Audience

2017· article· en· W2623717247 on OpenAlexafffund
Terrance Mok, Lora Oehlberg

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMateriality (auditing)Observational studyComputer scienceVideoconferencingHuman–computer interactionFace (sociological concept)MultimediaTeleconferenceObservational methods in psychologyAestheticsSociology

Abstract

fetched live from OpenAlex

We present an observational study of physical prototype critique that highlights some of the challenges of communicating physical behaviors and materiality at a distance. Geographically distributed open hardware communities often conduct user feedback and peer critique sessions via video conference. However, people have difficulty using current video conferencing tools to demonstrate and critique physical designs. To examine the challenges of remote critique, we conducted an observational lab study in which participants critiqued pairs of physical prototypes (prosthetic hands) for a face-to-face or remote collaborator. In both conditions, participants' material experiences were an important part of their critique, however their attention was divided between interacting with the prototype and finding strategies to communicate `invisible' features. Based on our findings, we propose design implications for remote collaboration tools that support the sharing of material experiences and prototype critique.

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)0.029
metaresearch head score (Gemma)0.156
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.156
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.006
Scholarly communication0.0060.005
Open science0.0040.006
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.032
GPT teacher head0.362
Teacher spread0.330 · 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 designQualitative
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

Citations19
Published2017
Admission routes2
Has abstractyes

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