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Record W4416533044 · doi:10.1111/grow.70084

Zoned in: The Influence of Land Use Policy on Canadian Craft Breweries

2025· article· en· W4416533044 on OpenAlexafffundabout
Roger M. Picton, Vanessa Mathews

Bibliographic record

VenueGrowth and Change · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsUniversity of ReginaTrent University
FundersSocial Sciences and Humanities Research Council of CanadaTrent UniversityUniversity of Regina
KeywordsZoningCraftBrewingLand useProcess (computing)

Abstract

fetched live from OpenAlex

ABSTRACT Municipal zoning bylaws have a considerable effect on the micro‐geographies of craft brewing. This paper examines the location of craft breweries across Canada and provides an analysis of how land use policy delineates the spatial location of craft breweries in six Canadian cities. Using the parcel level location of breweries as an entry point, our findings confirm how zoning bylaws have broadened the spatial parameters of brewing operations beyond industrial zones. However, while breweries are permitted/discretionary across a wider range of land uses, there is still a concentration of breweries around downtown, industrial, and special districts. Breweries remain largely prohibited from residential zones, part of a broader process of restricting any form of commercial and/or industrial use in single family zones. Our findings link with existing research on the locational dynamics of breweries. While the spatial parameters for zoning were recently broadened, most municipal standards collapse differences across breweries pertaining to orientation (production/consumption) and/or size of facility/output. Our research shows that, in these six cities, the potential for placemaking now extends beyond post‐industrial sites. We argue that municipalities should implement further regulatory measures to ensure compatibility between uses, by differentiating breweries according to their orientation and scale.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.419
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.024
GPT teacher head0.227
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

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

Citations1
Published2025
Admission routes3
Has abstractyes

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