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Record W2117894391 · doi:10.1177/0042098015594079

Waiting for the R train: Public transportation and employment

2015· article· en· W2117894391 on OpenAlexaff
Justin Tyndall

Bibliographic record

VenueUrban Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsUnemploymentPublic transportNeighbourhood (mathematics)LimitingBusinessLabour economicsDemographic economicsPublic policyWork (physics)Public economicsEconomicsEconomic growthTransport engineeringEngineering

Abstract

fetched live from OpenAlex

Expanding employment opportunities for citizens has become an increasingly central goal of public policy in the United States. Prior work has considered that the inability of households to spatially access jobs may be a driver of unemployment. The provision of public transportation provides a viable policy lever to increase the number of job opportunities available to households. Previous research has yielded mixed results regarding whether household location is an important factor in determining employment status. Several papers have identified mobility as a limiting factor for obtaining a job, particularly in regards to private vehicle ownership. The location of economically developed neighbourhoods and the citing of public transportation are conceivably codetermined, presenting an endogenous relationship. It is therefore unclear if public transportation access is actually contributing to neighbourhood job market outcomes. This paper will use the incidence of Hurricane Sandy striking New York City on 29 October 2012 and the resulting exogenous reduction in public transit access to particular neighbourhoods as a natural experiment to test for the effect of public transportation on employment outcomes. This study identifies a significant causal effect linking public transportation access to neighbourhood unemployment rates, particularly amongst subgroups dependent on public transit.

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 score0.493
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

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

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 head0.218
GPT teacher head0.378
Teacher spread0.160 · 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

Citations90
Published2015
Admission routes1
Has abstractyes

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