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Record W2072648767 · doi:10.1177/1071181312561089

Improving the Management of Interruption through the Working Awareness Interruption Tool: WAIT

2012· article· en· W2072648767 on OpenAlexaff
Meshael Alqahtani, Jonathan Histon

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicPersonal Information Management and User Behavior
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTask (project management)Air traffic controlComputer scienceProcess (computing)Situation awarenessContext (archaeology)Control roomOperator (biology)Context awarenessControl (management)Process managementTask managementAir traffic managementHuman–computer interactionRisk analysis (engineering)Systems engineeringEngineering

Abstract

fetched live from OpenAlex

Interruptions in time-critical, dynamic and collaborative environments, such as Air Traffic Control (ATC), can provide valuable, task-relevant information. However, they also negatively impact task performance by distracting the operator from on-going tasks and consuming “attention resources”. It is hypothesized that operators in these environments could better manage when interruptions occur if there were indications of the availability of a collaborator and the priority of an interruption. The Working Awareness Interruption Tool (WAIT) is being developed to support more efficient and appropriate interruption timing in the context of complex, real-time, distributed, human operator interactions. Prototypes for application in operational ATC displays are presented as well as techniques used to develop the design requirements. Feedback on the initial prototypes was solicited through a Participatory Design (PD) interview process with air traffic controllers. The implications of the findings for the feasibility of an interruption awareness tool in real ATC environments are discussed.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.199
GPT teacher head0.375
Teacher spread0.177 · 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 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

Citations2
Published2012
Admission routes1
Has abstractyes

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