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Record W2132765546 · doi:10.1145/2145204.2145286

A comparison of competitive and cooperative task performance using spherical and flat displays

2012· article· en· W2132765546 on OpenAlexaff
John Bolton, Kibum Kim, Roel Vertegaal

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsQueen's University
Fundersnot available
KeywordsTask (project management)Computer scienceAffordanceHuman–computer interactionSpace (punctuation)Engineering

Abstract

fetched live from OpenAlex

While large flat vertical displays may facilitate persistent public sharing of work, they may do so at a cost of limited personal display space when everyone can see each other's activity. By contrast, new form factors, such as spherical displays, support sharing display space by limiting the user's view to at most one hemisphere. In this paper, we investigate how different interactive large display form factors can support differences in sharing of information during competitive and cooperative task conditions. We implemented three different large display types: spherical, flat, and a flat display with divider. Results show that task performance of the flat display with divider did not differ significantly from that of the spherical display. Additionally, we implemented and compared three peeking techniques that facilitated sharing of information. Results show participants peeked significantly more in competitive tasks than they did in cooperative tasks. Usage of peeking techniques between the spherical display and the flat display with divider were similar, and distinct from that of the flat display. Not surprisingly, results show that the affordance of easily glancing at a partner's work on the flat display provided a significant advantage in cooperative tasks.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score0.340

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 communication0.0000.001
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 head0.030
GPT teacher head0.315
Teacher spread0.285 · 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 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

Citations29
Published2012
Admission routes1
Has abstractyes

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