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Record W2142362398 · doi:10.1080/1354983022000027578

Smart Growth and Sustainable Development: Challenges, solutions and policy directions

2002· article· en· W2142362398 on OpenAlexaboutno aff
Don Alexander, Ray Tomalty

Bibliographic record

VenueLocal Environment · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsnot available
Fundersnot available
KeywordsUrban sprawlSmart growthPer capitaSustainabilitySustainable developmentSpace (punctuation)Economic growthLand useRegional scienceGreen growthPolitical scienceEnvironmental planningPublic economicsGeographyEconomicsSociologyEcology

Abstract

fetched live from OpenAlex

In this paper, we focus on the issues related to development densities that emerged from our study of sprawl and development issues in three regions of British Columbia, Canada. We chose to focus on this aspect of the Smart Growth agenda because, while many of its other elements enjoy wide support across social interests, the goal of achieving a higher density urban fabric is highly controversial. We proceeded by collecting data on development densities and 13 indicators of community sustainability in 26 municipalities. The results suggest that the density of communities is associated with efficiencies in infrastructure and with reduced automobile dependence, with the ecological and economic implications which flow from that. However, it does not necessarily correlate with greater affordability of housing or more access to green space. In fact, if anything, we discovered a negative relationship between housing affordability and green space per capita and higher land-use densities. In a second stage of the research, we conducted a qualitative analysis of a subset of six municipalities and identified key policy issues for moving ahead with the Smart Growth agenda. The paper concludes with a discussion of the policy issues that emerged from these case studies.

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)0.011
metaresearch head score (Gemma)0.008
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: Review · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0050.015
Scholarly communication0.0220.022
Open science0.0030.013
Research integrity0.0140.011
Insufficient payload (model declined to judge)0.0180.003

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 head0.013
GPT teacher head0.177
Teacher spread0.164 · 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
GenreReview

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

Citations122
Published2002
Admission routes1
Has abstractyes

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