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Record W2807893527 · doi:10.1080/17549175.2018.1484793

Is there suitable housing near work? The impact of housing suitability on commute distances in Montreal, Toronto, and Vancouver

2018· article· en· W2807893527 on OpenAlexafffundabout
Markus Moos, Nick Revington, Tristan Wilkin

Bibliographic record

VenueJournal of Urbanism International Research on Placemaking and Urban Sustainability · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsUrban sprawlSustainabilityOperationalizationWork (physics)Stock (firearms)Urban planningNeighbourhood (mathematics)BusinessUrban sustainabilitySmart growthSubdivisionEnvironmental planningGeographyCivil engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

This paper makes a novel contribution by examining the impacts of housing suitability on the commute. Smart Growth and related planning policies have contributed to higher residential densities with the aim to reduce commute distances and enhance urban sustainability. While important in terms of alleviating sprawl, reductions in space accompanying increases in densities may not be suitable for larger households. If households instead commute longer distances, the sustainability objective of minimizing commute distances is undercut. We operationalize housing suitability at the household level in different ways, analysing the characteristics of housing available near the place of work in relation to the housing suitability needs based on household characteristics. Regardless of the measure used, the better the match between workers’ housing suitability needs and the housing stock available near work, the shorter the commute. The paper uniquely highlights the importance of explicitly considering housing suitability in planning for sustainability.

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)0.010
metaresearch head score (Gemma)0.002
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 score0.029
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.420
Teacher spread0.373 · 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

Citations6
Published2018
Admission routes3
Has abstractyes

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