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Record W2178080895 · doi:10.11575/prism/30504

Interactive Tables in the Wild - Visitor Experiences with Multi-Touch Tables in the Arctic Exhibit at the Vancouver Aquarium

2010· article· en· W2178080895 on OpenAlexfundaboutno aff
Uta Hinrichs, Sheelagh Carpendale

Bibliographic record

VenueOpen MIND · 2010
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsVisitor patternArcticGeographyTable (database)Computer scienceVisual artsComputer graphics (images)World Wide WebArtOceanographyGeologyDatabaseProgramming language

Abstract

fetched live from OpenAlex

This report describes and discusses the findings from a field study that was conducted at the Vancouver Aquarium to investigate how visitors explore and experience large horizontal multi-touch tables as part of public exhibition spaces. The study investigated visitors’ use of two different tabletop applications—the Collection Viewer and the Arctic Choices table—that are part of the Canada’s Arctic exhibition at the Vancouver Aquarium. Our findings show that both tabletop exhibits enhanced the exhibition in different ways. The Collection Viewer table evoked visitors curiosity by presenting visually interesting information and engaged by supporting lightweight, playful, and open-ended information exploration. The Arctic Choices table enabled visitors to explore a variety of information about environmental and political changes within the Arctic in depth by providing detailed data visualizations. The application triggered a lot of insightful discussions among visitors. Our study findings include a discussion of the factors that attracted visitors’ attention and triggered interaction with both tabletop exhibits, the character and duration of information exploration, general exploration strategies, and factors that triggered social and collaborative information exploration. We also discuss usability issues of both tabletop applications alongside possible solutions.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0030.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.291
Teacher spread0.273 · 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 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

Citations4
Published2010
Admission routes2
Has abstractyes

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