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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 OpenAlex
Steven Farber, Maria Grandez

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

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.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.039
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