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Record W4379958678 · doi:10.1002/uar2.20041

Planning the foodshed: Rural and peri‐urban factors in local food strategies of major cities in Canada and the United States

2023· article· en· W4379958678 on OpenAlexafffundabout
Kerstin Schreiber, Klara J. Winkler, Killian Abellon, Graham K. MacDonald

Bibliographic record

VenueUrban Agriculture & Regional Food Systems · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec-Société et CultureMcGill University
KeywordsFood systemsFood processingCorporate governanceBusinessIndigenousEnvironmental planningWorkforceEconomic growthGeographyPolitical scienceAgricultureFood securityEconomics

Abstract

fetched live from OpenAlex

Abstract Many North American cities seek to increase access to locally sourced foods. But to what degree are cities planning for and supporting peri‐urban and rural food production? We examined this question by analyzing 25 documents from 22 major United States and Canadian cities (>300,000 residents) with local food strategies or action plans, focusing on social, ecological, and technological factors relevant to local food production. Our review suggests that the following topics are often overlooked: farmland access and quality, farm viability, agricultural training and workforce, environmental and public health, processing infrastructure, climate change adaptation, and particular needs pertinent to those concerns among marginalized groups, including Black, Indigenous, and People of Color. Just under half of the cities defined how success or progress toward reaching their food‐related goals would be monitored and measured. Many municipalities considered collaboration as an important governance tool for realizing their strategies toward local food systems, including with actors from within the city and beyond, as cities’ governance scope, resources, and power are often limited. Besides illustrating ongoing municipal efforts to enhance local food systems, our study identifies focus areas in food policy and planning to avoid overlooking social and environmental trade‐offs in local foodsheds, including potentially overestimating self‐sufficiency capacity.

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 score0.326
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.190
Teacher spread0.173 · 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

Citations11
Published2023
Admission routes3
Has abstractyes

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