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Record W6928785109 · doi:10.3886/e180701v1

Data and Code for Shaping the Habits of Teen Drivers

2024· dataset· en· W6928785109 on OpenAlex

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

VenueICPSR Data Holdings · 2024
Typedataset
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsIntervention (counseling)EveningCrashCode (set theory)Poison controlHuman factors and ergonomics

Abstract

fetched live from OpenAlex

This repository provides the Stata code and the non-confidential data sources for the article "Shaping the Habits of Teen Drivers" published in the American Economic Journal: Economic Policy. We provide the abstract of the paper below:<br><br>We show that a targeted law can modify teens’ risky behavior. We examine the effects of an Australian intervention banning first-year drivers from driving late at night with multiple peers, which had accounted for one-fifth of their traffic fatalities. Using data on individual drivers linked to crash outcomes, we find the reform more than halves targeted crashes, casualties and deaths. There are large positive spillovers through lower crashes earlier in the evening and beyond the first year, suggesting broad and persistent declines in high-risk driving. Overall, the targeted intervention delivers gains comparable to harsher restrictions that delay teen driving.<br>

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.003
Threshold uncertainty score0.899

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0040.004
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 head0.158
GPT teacher head0.345
Teacher spread0.187 · 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