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Record W1582398188 · doi:10.1002/jae.2449

Successful Scientific Replication and Extension of Levitt (2008): Child Seats are Still No Safer Than Seat Belts

2015· article· en· W1582398188 on OpenAlexaff
Lauren Jones, Nicolas R. Ziebarth

Bibliographic record

VenueJournal of Applied Econometrics · 2015
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsOntario Brain InstituteUniversity of Toronto
Fundersnot available
KeywordsSAFERChild safetyReplication (statistics)Extension (predicate logic)ReplicateSeat beltPsychologyDemographic economicsComputer securityEnvironmental healthBusinessMedicineComputer scienceEconomicsEngineeringStatisticsMathematics

Abstract

fetched live from OpenAlex

Summary Using US fatality data from 1975 to 2003, Levitt (Evidence that seat belts are as effective as child safety seats in preventing death for children aged two and up, Review of Economics and Statistics 2008; 90 (1): 158–163) shows that child safety seats do not significantly reduce fatalities for children aged 2–6 years as compared to standard seat belts. Although we were unable to gain access to the original programs and dataset used, we were able to replicate Levitt's ( ) findings almost exactly. We extend Levitt ( ) by showing that the findings also hold for the years 2004–2011 despite changing driver characteristics and restraint use patterns. We fail to find evidence that SUVs provide additional safety for children. Copyright © 2015 John Wiley & Sons, Ltd.

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)0.000
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 score0.735
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.026
GPT teacher head0.214
Teacher spread0.188 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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