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Record W2021620362 · doi:10.1080/10447318.2012.715536

Guidelines for Designing Awareness-Augmented Mobile DUIs

2012· article· en· W2021620362 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Human-Computer Interaction · 2012
Typearticle
Languageen
FieldComputer Science
TopicUsability and User Interface Design
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsComputer scienceHuman–computer interactionMobile deviceHeuristicsWorkspaceCommon groundFace (sociological concept)User interfaceInternet privacyWorld Wide WebMultimediaArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

Colocated groups using mobile devices do not share all of the benefits of face-to-face collaborators. Close interaction requires application support for awareness features, allowing participants to establish common ground. Following an overview of research on awareness and grounding, the results of an informal user study are presented, which demonstrate how current systems can deter users from engaging in close collaboration. Literature on awareness provides hope for improving this situation, but a naive transfer to mobile distributed user interfaces will not necessarily succeed. From prior art, a concise list of guidelines has been compiled to assist designers in providing awareness information to users of shared mobile workspaces. These guidelines can also serve as heuristics for the evaluation of future systems. An example is provided to demonstrate how these guidelines can be applied to the development of features for providing awareness of current location and browsing history to colocated users of mobile distributed user interfaces.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.734
Threshold uncertainty score0.764

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 communication0.0000.003
Open science0.0010.000
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 head0.159
GPT teacher head0.431
Teacher spread0.272 · 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