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Record W2022317655 · doi:10.1080/13549839.2013.788489

The uneven geographies of community food initiatives in southwestern Ontario

2013· article· en· W2022317655 on OpenAlexaffabout
Erin Nelson, Irena Knežević, Karen Landman

Bibliographic record

VenueLocal Environment · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsMount Saint Vincent UniversityUniversity of Guelph
Fundersnot available
KeywordsRealmVariety (cybernetics)Food systemsSocial capitalGeographyHappeningCommunity developmentEconomic growthSubject (documents)Political scienceEconomic geographyRegional scienceEnvironmental planningSociologyFood securitySocial scienceEconomicsAgricultureArchaeologyHistory

Abstract

fetched live from OpenAlex

Data collected in 14 southwestern Ontario counties and regional municipalities demonstrated that the development of community food initiatives is not happening uniformly across the region. Rather, some areas (notably Wellington and Norfolk counties and Waterloo Region) are home to a wide variety of projects that, in many cases, are woven together into networks and enjoy relatively broad-based support from local communities. In contrast, in other places (for example, Dufferin, Elgin, and Kent counties), efforts to foster the development of alternative food systems are fewer and farther between, more fledgling in nature, and appear subject to more constraints than their counterparts in neighbouring parts of the region. This paper will explore the uneven geography of community food projects in southwestern Ontario, and discuss how the presence of social capital structured around an alternative food system vision can help expand the realm of possibility for such initiatives.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.161
Teacher spread0.150 · 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 designQualitative
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

Citations26
Published2013
Admission routes2
Has abstractyes

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