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Record W2993490778 · doi:10.36939/cjur/vol24no1/art8

Local Food System Planning: The Problem, Conceptual Issues, and Policy Tools for Local Government Planners

2015· article· en· W2993490778 on OpenAlexvenueno aff
R. Buchan

Bibliographic record

VenueCanadian journal of urban research · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsLocal governmentFood systemsNormativeConceptual frameworkBusinessEnvironmental planningGovernment (linguistics)Work (physics)AgricultureEnvironmental resource managementPolitical scienceFood securityEngineeringGeographyEconomicsSociologyPublic administration

Abstract

fetched live from OpenAlex

Local food system planning was identified in the late 1990s as an emerging and important urban planning object. Since then, little attention has been placed on identifying a robust and comprehensive understanding of the roles and tools local government can use in addressing their local food systems. The emerging literature identifi es problems with the dominant productionist agricultural system, addresses conceptual issues and often advances normative arguments in support of developing and supporting local food systems, but attention to the practical actions needed to address this issue on the ground have been limited. This paper provides an overview of the reported risks (such as water shortages, climate change, peak oil) associated with our dominant food systems, addresses the lack of attention to the importance of subscales within ‘local’ and definition of ‘local food,’ and it identifies the main reasons for considering local food systems as part of addressing the food system risks. Finally, it presents a policy framework along with tools and roles for local government to address local food systems within each of the framework’s categories. The principal purpose is to help advance the local food system work of planners in their North American communities.

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)0.003
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.112
GPT teacher head0.303
Teacher spread0.191 · 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 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

Citations19
Published2015
Admission routes1
Has abstractyes

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