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Record W1500955749

Challenging the Sprawl of Big Box Retail: The Smart Growth Approach to 'Zone it and They Will Come' Development

2002· article· en· W1500955749 on OpenAlex

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.

Bibliographic record

VenueSSRN Electronic Journal · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicProperty Rights and Legal Doctrine
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsUrban sprawlSmart growthZoningMetropolitan areaScope (computer science)Big dataLand usePoliticsBusinessRegional scienceGeographyPolitical scienceEngineeringCivil engineeringLawComputer science
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this paper is to outline the impacts of big box retail land uses on communities in North America and to explore legal and integrated land use strategies to address the problems associated with big box retail. While it is beyond the scope of this paper to detail strategies that counter the effects of superstores on regional and national labor markets, many of the land use solutions proposed in Part 3 inherently address these issues. In Part 1, I briefly outline the smart growth metropolitan development framework in which big box sprawl is situated, and in Part 2 set out the problems with big box stores. In Part 3, I present the jurisprudence of land use and zoning that frames how local governments and citizens can control retail development in communities. In Part 4, I further discuss specific strategies employed by states, local governments, and citizens to control big box sprawl. Finally, Part 5 is dedicated to case studies of integrated legal and political approaches that have resulted in smart growth solutions.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.927
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.238
Teacher spread0.203 · 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