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Record W4410859796 · doi:10.1080/07352166.2025.2507951

Transportation barriers and equity: Values and experiences of elected officials

2025· article· en· W4410859796 on OpenAlexafffundabout
Orly Linovski, Jennifer Dean, Samantha Leger, Isabel Cascante

Bibliographic record

VenueJournal of Urban Affairs · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of WaterlooUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEquity (law)BusinessPublic administrationPublic economicsPolitical scienceFinanceEconomicsLaw

Abstract

fetched live from OpenAlex

Despite the importance of elected officials in shaping transportation policies, there has been little direct research on their values and experiences. Understanding the values of elected officials is particularly important in the context of transportation equity, which is fundamentally concerned with the distribution of benefits across communities and geographies. This research draws on a survey and in-depth interviews with local elected officials in Canada to understand their experiences and how they view transportation equity issues. We find that elected officials have little direct experience with transportation barriers, with few experiencing issues such as unaffordability, disability, or harassment, and evidence that this influences their values related to equity. While there was widespread agreement with values such as procedural equity, there was limited support for prioritizing marginalized communities in transportation investments, and this support varied by experience and identities. These findings point to a significant disconnect between academic/professional discourses on transportation equity and the values of elected officials. This underscores the need for addressing political contexts in understanding transportation decision-making and offering deeper understandings of the motivations and values of elected officials.

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 score0.275
Threshold uncertainty score0.258

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 studies0.0000.001
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 head0.013
GPT teacher head0.312
Teacher spread0.299 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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