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Record W2943254865 · doi:10.1080/02697459.2019.1601800

An Analysis of the Influence of Smart Growth on Growth Patterns in Mid-Sized Canadian Metropolitan Areas

2019· article· en· W2943254865 on OpenAlex
Rylan Graham, Albert Tonghoon Han, Sasha Tsenkova

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePlanning Practice and Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSmart growthMetropolitan areaUrbanizationGrowth managementEconomic geographyGeographyRegional scienceCensusEconomic growthUrban planningEcologyEconomicsDemographyBiologySociology

Abstract

fetched live from OpenAlex

Since the late 1990s, Smart Growth has found broad acceptance within Canadian planning as a framework for sustainable urban development. Smart Growth emerged as a response to decades of dispersed and decentralized growth that dominated urbanization patterns in North America post-WWII. Through a series of spatial analysis methods, this research examines whether Smart Growth has influenced growth patterns of six mid-sized Canadian census metropolitan areas from 1990 to 2010. Findings of this research suggest that municipalities and regions have adopted policies consistent with Smart Growth, however, its influence on dispersed patterns of spatial growth has been limited.

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)0.004
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 score0.361
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
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 head0.048
GPT teacher head0.407
Teacher spread0.359 · 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