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Record W1987319212 · doi:10.1080/17549175.2013.771694

Neighbourhood type and walkshed size

2013· article· en· W1987319212 on OpenAlexaffabout
Beverly A. Sandalack, F.G. Alaniz Uribe, A. Eshghzadeh Zanjani, Alan Shiell, Gavin R. McCormack, Patricia K. Doyle–Baker

Bibliographic record

VenueJournal of Urbanism International Research on Placemaking and Urban Sustainability · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWalkabilityNeighbourhood (mathematics)GeographyTransport engineeringUrban planningRegional scienceComputer scienceBuilt environmentMathematicsCivil engineeringEngineering

Abstract

fetched live from OpenAlex

Neighbourhood block pattern has been hypothesized to be a major factor in providing residents with the potential for walking. However, without an accurate tool to measure walksheds, this was not verifiable. Recent research, a portion of the EcoEUFORIA (Economic Evaluation of Urban Form to Increase Activity) project, provided techniques for accurately measuring walksheds, and allowed statistical analysis of a large data-set representing all the neighbourhoods in Calgary, Canada. This research demonstrates that walkshed size varies among neighbourhood types, with the grid block pattern being the most walkable, and the curvilinear pattern the least. Despite the growing body of knowledge regarding walkability, the prevailing practice is to continue to develop the less walkable curvilinear forms. This research has the potential to influence the development of planning policies that promote more walkable neighbourhood design, in that it illustrates clearly, and using a large data-set, the relationships between neighbourhood form and walkability.

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)0.006
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.410
Teacher spread0.368 · 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

Citations28
Published2013
Admission routes2
Has abstractyes

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