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Record W2288534356 · doi:10.1080/00045608.2015.1113118

Revisiting Gender, Race, and Commuting in New York

2016· article· en· W2288534356 on OpenAlexaff
Valerie Preston, Sara McLafferty

Bibliographic record

VenueAnnals of the American Association of Geographers · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsYork University
Fundersnot available
KeywordsMetropolitan areaDemographic economicsRace (biology)Ethnic groupPublic transportInvestment (military)InequalityGeographySociologyEconomicsPolitical sciencePoliticsGender studies

Abstract

fetched live from OpenAlex

In the 1990s, many women commuted shorter distances and less time than men, and research underscored the pernicious effects of racial and ethnic segregation and access to transportation on minority women's commuting. Since then, growing income inequality and the bifurcation of employment between well-paid and secure jobs and a growing number of insecure and poorly paid jobs have been accompanied by the concentration of jobs at central and suburban locations and the transformation of women's roles in the labor market. We investigate some of the geographical implications of these trends by analyzing commuting in the New York metropolitan region. In 2010, gender and race differences in commuting varied across the metropolitan area. Regression analysis demonstrates that the impacts of wages and household composition on commuting differ between the highly valued center that has benefited from private and public investment, the suburbs where traditional gender roles persist, and the deteriorating inner ring where minority women still commute long times on slow public transit. The findings highlight racial and gender disparities in geographical access to employment within the metropolitan region.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.033
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.047
GPT teacher head0.329
Teacher spread0.283 · 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

Citations67
Published2016
Admission routes1
Has abstractyes

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