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Record W3199112799 · doi:10.1080/02697459.2021.1979786

Smart Growth in Canada’s Provincial North

2021· article· en· W3199112799 on OpenAlexafffundabout
Mark Groulx, Kristen Kieta, Matthew Rempel, Darwin Horning, Kyrke Gaudreau

Bibliographic record

VenuePlanning Practice and Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Northern British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Northern British Columbia
KeywordsSmart growthNeighbourhood (mathematics)SustainabilityContext (archaeology)Urban sustainabilitySmart citySustainable growth rateGrowth managementGeographyCapital (architecture)Economic geographyCapital cityRegional scienceEnvironmental planningUrban planningEconomic growthBusinessEconomicsLand useInternet of ThingsEngineeringCivil engineeringComputer scienceEcologyComputer security

Abstract

fetched live from OpenAlex

Smart growth promotes urban sustainability by encouraging increased densities, mixed use, walkable design, and access to diverse transportation and housing options. This study applies literature-derived indicators to examine urban change in the city of Prince George; British Columbia’s northern capital. Findings illustrate that key growth nodes have largely performed (e.g., densified) at or below the level of their surrounding neighbourhood over time despite a robust set of policy tools associated with smart growth. This research is one of few to examine smart growth in a northern urban context, and situates the concept within the slow growth/no growth realities of many rural and remote regions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.694

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0130.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.086
GPT teacher head0.406
Teacher spread0.320 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations8
Published2021
Admission routes3
Has abstractyes

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