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Record W596025831

Beyond TOD: Establishing Upper Bounds of Transit-Supportive Neighborhoods

2014· article· en· W596025831 on OpenAlexaboutno aff
Susan J. Petheram, Arthur C. Nelson, Matt Miller, Reid Ewing

Bibliographic record

VenueTransportation Research Board 93rd Annual MeetingTransportation Research Board · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsTransit-oriented developmentQuarter (Canadian coin)Transit (satellite)MileTransport engineeringGeographySingle familyCore (optical fiber)Last mile (transportation)Rail transitBusinessPublic transportEngineeringTelecommunicationsFinance
DOInot available

Abstract

fetched live from OpenAlex

There is a substantial body of literature regarding the price effects of light rail transit accessibility on residential properties. However, few use market response to establish the upper bounds of transit-supportive neighborhoods, which extend beyond development in the core station area. This paper closes some of this gap in the research. The authors estimate the association between TRAX, the light rail system serving Salt Lake County, Utah, and the value of different sized single-family residential lots. Using one-quarter mile distance-bands from light rail stations out to one and one-half miles, the authors evaluate variations in response according to small and conventional lot size categories. Controlling for structural, neighborhood and location characteristics, the authors find a generally positive relationship between TRAX station proximity and residential values out to one and one-quarter miles. However, a negative impact is captured for lots located in the core station area. Variations in value are present in the different lot size categories, and implications for planning transit-supportive neighborhoods are offered.

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)0.015
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.318
Teacher spread0.271 · 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.

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

Citations0
Published2014
Admission routes1
Has abstractyes

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Same venueTransportation Research Board 93rd Annual MeetingTransportation Research BoardSame topicHousing Market and EconomicsFrench-language works237,207