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Record W2039450733 · doi:10.1145/2317956.2318025

Territoriality and behaviour on and around large vertical publicly-shared displays

2012· article· en· W2039450733 on OpenAlexaff
Alec Azad, Jaime Ruiz, Daniel Vogel, Mark Hancock, Edward Lank

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTerritorialityHorizontal and verticalComputer scienceOrientation (vector space)Space (punctuation)Field (mathematics)Identification (biology)Human–computer interactionFunction (biology)PsychologyCommunicationMathematicsGeometry

Abstract

fetched live from OpenAlex

We investigate behaviours on, and around, large vertical displays during concurrent usage. Using an observational field study, we identify fundamental patterns of how people use existing public displays: their orientation, positioning, group identification, and behaviour within and between social groups just-before, during, and just-after usage. These results are then used to motivate a controlled experiment where two individuals or two pairs of individuals complete tasks concurrently on a simulated large vertical display. Results from our controlled study demonstrates that vertical surface territories are similar to those found in horizontal tabletops in function, but their definitions and social conventions are different. In addition, the nature of use-while-standing systems results in more complex and dynamic physical territories around the display. We show that the anthropological notion of personal space must be slightly refined for application to vertical displays.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.278
Teacher spread0.260 · 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 designObservational
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

Citations49
Published2012
Admission routes1
Has abstractyes

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