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Record W3082767132 · doi:10.32866/001c.16724

The Impact of Implementing Public Bicycle Share Programs on Bicycle Crashes

2020· article· en· W3082767132 on OpenAlex
Michael Branion-Calles, Kate Hosford, Meghan Winters, Lise Gauvin, Daniel Fuller

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueFindings · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversité de MontréalSimon Fraser UniversityMemorial University of NewfoundlandToronto Metropolitan University
Fundersnot available
KeywordsOddsCrashTransport engineeringPublic transportPoison controlDifference in differencesInjury preventionHuman factors and ergonomicsGeographyEngineeringBusinessEnvironmental healthMedicineComputer scienceLogistic regressionStatisticsMathematics

Abstract

fetched live from OpenAlex

A docked public bicycle share program (PBSP) makes bicycles available to the public. There is limited evidence on the impact of PBSPs on safety. We estimated the impacts of implementing a PBSP on the likelihood of bicycle crashes using a difference in differences approach with repeated cross-sectional survey data (self-reported crashes) collected in 8 Canadian and US cities, from 2012-2014. Relative to control cities (Detroit, Philadelphia, Vancouver), we found that the odds of reporting a bicycling crash did not change after implementing a PBSP (New York, Chicago) and were lower in cities that had existing PBSPs (Boston, Montreal, Toronto).

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.080
GPT teacher head0.358
Teacher spread0.278 · 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