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Record W2995628320 · doi:10.1080/17457300.2019.1694042

Driver education: how effective?

2019· review· en· W2995628320 on OpenAlexaboutno aff
Brian O’Neill

Bibliographic record

VenueInternational Journal of Injury Control and Safety Promotion · 2019
Typereview
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsnot available
Fundersnot available
KeywordsPublicityCrashPoison controlSuicide preventionInjury preventionHuman factors and ergonomicsOccupational safety and healthEngineeringBusinessPolitical scienceMedicineEnvironmental healthMarketingLawComputer science

Abstract

fetched live from OpenAlex

In the early 20th century, the numbers of motor vehicles in use grew rapidly in the USA, Canada, and many European countries. By the 1930s, the number of automobile crashes and the resulting deaths and injuries was a significant problem and various safety organizations tried to address it with education and publicity programs aimed at changing driver behaviour. It is not clear when the high crash risks of young drivers were first identified, but in the early 1930s driver education courses began to be offered in US high schools (feasible because US licensing ages were 16 or younger) and soon such courses were being touted (with no evidence) as 'the most obvious way' to reduce traffic crashes. Over the years many claims were made for the effectiveness of high school driver education, however, it was not until the late 1960s that competent research studies (including randomized control trials) were undertaken. The consistent findings from these studies have been that high school driver education does not reduce crashes. Furthermore, the trained students get their licenses sooner, and because teenagers have very high crash risks, the net result of high school driver education is increased numbers of crashes.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.270
Teacher spread0.261 · 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 designOther design
Domainnot available
GenreReview

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
Published2019
Admission routes1
Has abstractyes

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