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Record W2062402846 · doi:10.1177/1541931214581454

Driver Engagement in Notifications

2014· article· en· W2062402846 on OpenAlex
Wayne C.W. Giang, Liberty Hoekstra-Atwood, Birsen Donmez

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

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2014
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSmartwatchWearable computerComputer scienceHuman–computer interactionWearable technologyExploratory researchInternet privacyDisplay sizeSmartphone applicationMultimediaEmbedded systemOperating systemDisplay device

Abstract

fetched live from OpenAlex

Smartwatches and other wearables are being developed for the consumer market and will most likely be used by drivers, but there is little investigation into their influence on driver behaviour. Smartwatches are able to provide certain smartphone functionalities. For example, they can provide notifications, such as text mes-sages. Because watches are always “on-hand”, drivers may find it easier and be more compelled to interact with them in comparison to smartphones. We conducted an exploratory driving simulator study to compare a smartwatch and a smartphone in terms of time to engagement with the device and drivers’ glance patterns. The results show that participants (n=6) chose to engage with the smartwatch faster than with the smartphone, but took longer to read notifications. The smartwatch also led to a larger number of glances greater than 2 seconds than the smartphone. Further investigation of the effects on driving performance is required.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.376

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 communication0.0000.000
Open science0.0000.000
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 head0.028
GPT teacher head0.300
Teacher spread0.271 · 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