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Record W2588112558 · doi:10.1145/2998181.2998355

Collaboration And Awareness Amongst Flight Attendants

2017· article· en· W2588112558 on OpenAlexaff
Stephanie Wong, Carman Neustaedter

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsUsabilityWork (physics)Knowledge managementService (business)Computer scienceBusinessEngineeringHuman–computer interactionMarketing

Abstract

fetched live from OpenAlex

Collaboration is a core component of work activities amongst flight attendants as they work to promote onboard safety and a high level of customer service. Yet we know little of how flight attendants collaborate and whether or not technology adequately supports their practices. Through an interview study with flight attendants, we explored their collaborative practices and processes and how technology aided such practices. While technologies like interphones and flight attendant call buttons acted as collaboration tools, we identified instances where the usability and functionality of these devices were the main barriers for maintaining efficient communication, situation awareness, and information exchange. Our findings inform the design of future technologies for enhancing communication and collaboration in an aircraft setting amongst flight attendants with an emphasis on real time information access and direct communication between flight attendants regardless of their location.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.001

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 head0.039
GPT teacher head0.415
Teacher spread0.376 · 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; both teacher heads agree on what is shown here.

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

Citations14
Published2017
Admission routes1
Has abstractyes

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