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Record W2078726048 · doi:10.1145/2702123.2702506

Mapping out Work in a Mixed Reality Project Room

2015· article· en· W2078726048 on OpenAlexafffund
Derek Reilly, Andy Echenique, Andy Wu, Anthony Tang, W. Keith Edwards

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of CalgaryDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaSteelcaseNational Science Foundation
KeywordsSituatedTask (project management)Human–computer interactionMixed realityComputer scienceVirtual realityCognitionSpace (punctuation)Work (physics)Affect (linguistics)Interface (matter)Physical spaceCognitive mapVirtual spaceTask analysisPsychologyArtificial intelligenceGeographyEngineeringCommunication

Abstract

fetched live from OpenAlex

We present results from a study examining how the physical layout of a project room and task affect the cognitive maps acquired of a connected virtual environment during mixed-presence collaboration. Results indicate that a combination of physical layout and task impacts cognitive maps of the virtual space. Participants did not form a strong model of how different physical work regions were situated relative to each other in the virtual world when the tasks performed in each region differed. Egocentric perspectives of multiple displays enforced by different furniture arrangements encouraged cognitive maps of the virtual world that reflected these perspectives, when the displays were used for the same task. These influences competed or coincided with document-based, audiovisual and interface cues, influencing collaboration. We consider the implications of our findings on WYSIWIS mappings between real and virtual for mixed-presence collaboration.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.185

Codex and Gemma teacher scores by category

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

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 head0.095
GPT teacher head0.288
Teacher spread0.194 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations13
Published2015
Admission routes2
Has abstractyes

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