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Record W2073143962 · doi:10.1155/2012/137686

Comparing Horizontal and Vertical Surfaces for a Collaborative Design Task

2012· article· en· W2073143962 on OpenAlexafffund
Brianna Potvin, Colin Swindells, Melanie Tory, Margaret‐Anne Storey

Bibliographic record

VenueAdvances in Human-Computer Interaction · 2012
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHorizontal and verticalOrientation (vector space)Task (project management)Face (sociological concept)PsychologyHuman–computer interactionVertical orientationSocial psychologyComputer scienceCognitive psychologyGeometryMathematicsEngineeringSociology

Abstract

fetched live from OpenAlex

We investigate the use of different surface orientations for collaborative design tasks. Specifically, we compare horizontal and vertical surface orientations used by dyads performing a collaborative design task while standing. We investigate how the display orientation influences group participation including face-to-face contact, total discussion, and equality of physical and verbal participation among participants. Our results suggest that vertical displays better support face-to-face contact whereas side-by-side arrangements encourage more discussion. However, display orientation has little impact on equality of verbal and physical participation, and users do not consistently prefer one orientation over the other. Based on our findings, we suggest that further investigation into the differences between horizontal and vertical orientations is warranted.

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)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.040
GPT teacher head0.332
Teacher spread0.292 · 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 designBench or experimental
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

Citations17
Published2012
Admission routes2
Has abstractyes

Explore more

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