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Record W2587957226 · doi:10.4074/s0761898016002090

Better Integrating Driver Education and Training within a New Graduated Driver Licensing Framework in North America

2016· article· en· W2587957226 on OpenAlexaff
Daniel R. Mayhew, Allan F. Williams, Robyn Robertson, Ward Vanlaar

Bibliographic record

VenueRecherche Transports Sécurité · 2016
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsTraffic Injury Research Foundation
Fundersnot available
KeywordsPaceCrashEngineeringTransport engineeringComputer science

Abstract

fetched live from OpenAlex

Graduated Driver Licensing (GDL) and driver education are two safety measures for teen and new drivers that have been widely adopted in North America, often in isolation from one another. Driver education pre-dated GDL and has remained relatively unchanged from its inception, whereas GDL has undergone enhancements although the pace of change has slowed down. GDL has proven safety effectiveness which has not been the case for driver education, although a few recent studies have had promising results on the safety value of both traditional and nontraditional programs. This paper makes the case for integrating driver education with enhanced GDL to better address the elevated crash risk of teen drivers. It recommends that driver education be multi-phased and more closely aligned with the tiered structure of GDL and that the National Driver Education Standards (NDES) become the new “norm” for driver education. It also provides guidance for improving the content and delivery of driver education, including the use of nontraditional teaching techniques and training approaches. Future efforts to integrate and improve GDL and driver education, however, need to be researched using solid evaluation designs to ensure they have safety effects and contribute to GDL’s overall success.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.906
Threshold uncertainty score0.851

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 integrity0.0000.001
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 head0.045
GPT teacher head0.266
Teacher spread0.221 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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