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Record W2949483509 · doi:10.1145/3300961

SMAC

2019· article· en· W2949483509 on OpenAlexaff
Zhen Li, Michelle Annett, Ken Hinckley, Daniel Wigdor

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicPersonal Information Management and User Behavior
Canadian institutionsCanadian Rheumatology AssociationUniversity of Toronto
Fundersnot available
KeywordsProxemicsHuman–computer interactionWorkflowDeskComputer scienceWork (physics)GazeEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Prior research has demonstrated that users are increasingly employing multiple devices during daily work. Currently, devices such as keyboards, cell phones, and tablets remain largely unaware of their role within a user's workflow. As a result, transitioning between devices is tedious, often to the degree that users are discouraged from taking full advantage of the devices they have within reach. This work explores the device ecologies used in desk-centric environments and complies the insights observed into SMAC, a simplified model of attention and capture that emphasizes the role of user-device proxemics, as mediated by hand placement, gaze, and relative body orientation, as well as inter-device proxemics. SMAC illustrates the potential of harnessing the rich, proxemic diversity that exists between users and their device ecologies, while also helping to organize and synthesize the growing body of literature on distributed user interfaces. An evaluation study using SMAC demonstrated that users could easily understand the tenants of user- and inter-device proxemics and found them to be valuable within their workflows.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.310
GPT teacher head0.458
Teacher spread0.148 · 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.

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

Citations5
Published2019
Admission routes1
Has abstractyes

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