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Record W2617902224 · doi:10.22230/cjc.2017v42n2a3125

Older People Driving a High-Tech Automobile: Emergent Driving Routines and New Relationships with Driving

2017· article· en· W2617902224 on OpenAlexaffvenue
Jessica A. Gish, Amanda Grenier, Brenda Vrkljan, Benita Van Miltenburg

Bibliographic record

VenueCanadian Journal of Communication · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEmbodied cognitionControl (management)Task (project management)Car drivingHigh techDriving factorsHuman–computer interactionEngineeringPsychologyComputer scienceAutomotive engineeringPolitical scienceArtificial intelligenceSystems engineering

Abstract

fetched live from OpenAlex

Advanced vehicle technologies (AVTs) (e.g., lane departure warning, blind spot monitoring) are sophisticated computer and electronically mediated communications that provide information to users, and, at times, assume control over parts of the driving task (e.g., automated braking). This article examines how AVTs are refashioning older people’s embodied relationships with driving, including driving routines, skills, sensuous dispositions, and modes of control that are considered integral to driving. Results from interviews with 35 older drivers driving a high-tech car call attention to the opportunities and challenges that entanglements with AVTs can present for aging drivers.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.280
Teacher spread0.252 · 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

Citations10
Published2017
Admission routes2
Has abstractyes

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