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Record W4388638756 · doi:10.1080/23792949.2023.2261530

Planning for accessibility: the divide between research and policy in the promotion of equitable mobility

2023· article· en· W4388638756 on OpenAlexaff
Ignacio Tiznado-Aitken, Giovanni Vecchio, Rodrigo Mora, Lucaz Gonzalez, Catalina Marshall

Bibliographic record

VenueArea Development and Policy · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersCentro de Desarrollo Urbano Sustentable
KeywordsEquity (law)Metropolitan areaScope (computer science)InequalityMultidisciplinary approachPublic economicsRegional sciencePublic relationsMobilitiesPolitical scienceBusinessEconomic growthSociologyGeographyEconomicsComputer scienceSocial science

Abstract

fetched live from OpenAlex

Mobility-related social inequalities are receiving increasing attention from planning research and practice. Nevertheless, research seems to have a limited impact on urban policies addressing mobility. Using Santiago de Chile as a case study, the paper discusses the existing gaps between research on mobility-related equity concerns and existing policies and plans addressing urban mobility operating at national, metropolitan and municipal scales. An equity-based comparison is performed for different spatial planning instruments, exploring guiding concepts and deriving proposals through content analysis. The findings show that there is a comprehensive and multidisciplinary body of literature in Santiago on mobility and equity, approaching several dimensions of mobility, accessibility and social exclusion in relation to different population groups. However, the series of discourses, norms and actions (policies and programmes) operating at different planning scales lack coherence and address only some of the dimensions identified in the literature. Current plans and policies in Santiago have a limited scope and are difficult to modify, questioning their effectiveness for understanding and tackling mobility-related equity concerns.

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)0.007
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 score0.081
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
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)0.0000.000

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 head0.283
GPT teacher head0.474
Teacher spread0.192 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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