MétaCan
Menu
Back to cohort
Record W1974993233 · doi:10.1068/a37414

Wasted Density? The Impact of Toronto's Residential-Density-Distribution Policies on Public-Transit Use and Walking

2006· article· en· W1974993233 on OpenAlexaffabout
Pierre Filion, Kathleen McSpurren, Brad Appleby

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPublic transportMetropolitan areaNeighbourhood (mathematics)Compact cityUrban densityCrowdingGeographyTransport engineeringPublic useEconomic geographyRegional sciencePolitical scienceUrban planningEngineeringCivil engineeringPsychologyMathematics

Abstract

fetched live from OpenAlex

Although the Toronto metropolitan region performs well relative to its North American counterparts in terms of density and public-transit use, it does not derive as much walking and public-transit patronage benefit from its high-residential-density areas as it could. The impact of residential density on journey patterns is limited by an imperfect juxtaposition of density and public-transit service peaks. Another impediment is the difficulty of associating density with other variables needed for it to translate into increased walking and public-transit modal shares. We attribute this situation to insufficient planning capacity owing in large part to generalized neighbourhood opposition to high-density residential developments and disagreement between levels of government. In this paper we both narrate events of relevance to the distribution of high residential density over the last five decades and analyze present relationships between high-density areas and journey patterns. We conclude by discussing the possibility of achieving residential-density layouts and distributions that are more conducive to walking and public-transit use than the tower-in-the-park model and the scattering of high-density pockets, both of which predominate in Toronto.

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)0.000
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 score0.038
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.018
GPT teacher head0.248
Teacher spread0.230 · 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

Citations46
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueEnvironment and Planning A Economy and SpaceSame topicUrban Transport and AccessibilityFrench-language works237,207