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Record W2666402525 · doi:10.5198/jtlu.2017.980

Transit accessibility, land development and socioeconomic priority: A typology of planned station catchment areas in the Greater Toronto and Hamilton Area

2017· article· en· W2666402525 on OpenAlexafffundabout
Steven Farber, Maria Grandez

Bibliographic record

VenueJournal of Transport and Land Use · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto ScarboroughUniversity of Toronto
KeywordsCatchment areaRedevelopmentSocioeconomic statusLand useEnvironmental planningTransport engineeringDrainage basinGeographyTypologyTransit (satellite)Land-use planningUnavailabilityBusinessPublic transportCivil engineeringPopulationEngineeringCartographyEnvironmental health

Abstract

fetched live from OpenAlex

The Greater Toronto and Hamilton Area is in the process of implementing a wide array of transit expansion projects. Despite being an important evaluator of transit efficacy, accessibility is not a typical variable included in the business cases of the local planning authorities. We address this shortcoming by computing current and future accessibility scores for each proposed transit route and station. Our results are compared against measures of availability of developable land within station catchment areas and the socioeconomic priority of populations residing within catchment areas. A typology of station types is produced via a multi-criteria analysis, and this is further used to assess the efficacy of the transit plans in meeting the redevelopment and intensification goals and social priorities in the region. We are able to conclude that significant mismatches between accessibility and developable land exist. Furthermore, there is a lack of alignment between accessibility and socioeconomic priority; however, where these two criteria align, risks of redevelopment-based gentrification are low, due to the unavailability of readily developable land in these station catchment areas.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.317
Threshold uncertainty score0.637

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.308
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), 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

Citations58
Published2017
Admission routes3
Has abstractyes

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