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Record W2021133340 · doi:10.5304/jafscd.2013.032.005

Toronto Farmers' Markets: Towards Cultural Sustainability?

2013· article· en· W2021133340 on OpenAlexaffabout
Deborah Bond, Robert Feagan

Bibliographic record

VenueJournal of Agriculture Food Systems and Community Development · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsGeneral partnershipSustainabilityIncentiveBusinessVendorFocus groupMulticulturalismAgricultureWork (physics)MarketingPublic relationsDiversity (politics)Food systemsEconomic growthPolitical scienceFood securityGeographyEconomicsEngineeringFinance

Abstract

fetched live from OpenAlex

This paper accomplishes two interrelated objec­tives. The first is a qualitative assessment of the level of interest and accommodation of culturally appropriate foods at 14 farmers' markets (FMs) within the multicultural urban core of Toronto, Ontario. The second objective acquires insights from key public "food commentators" and from new agricultural initiatives in this region that help us develop recommendations relative to the outcomes of the first objective. Results from the first part of the study indicate that the level of provision of culturally appropriate foods at these FMs in Toronto is at an embryonic stage. The results of the second part of the study point to a range of initiatives oriented to support increased provision and accommodation of culturally appropriate foods along the FM chain, while also pointing to the existing constraints to these efforts. Broad recommendations include supporting emerging agricultural innovations and the diversity of partnership possibilities in this work; increasing awareness of such efforts for cultural sustainability objectives; and attending to FM vendor needs around this shift in demand. Policy efforts could focus on incentives and training for agricultural nonprofits and for partnership building, on supporting cultural groups hoping to increase their access to such foods grown in this region, and on existing farmers and those interested in farm access for these purposes. At the same time, advocacy for such shifts needs to recognize challenges in Canada to growing such new crops, the reality of farmer/vendor bottom lines, and broader global food system realities that constrain such efforts.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.012
GPT teacher head0.207
Teacher spread0.195 · 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

Citations5
Published2013
Admission routes2
Has abstractyes

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