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Record W4328119933 · doi:10.29173/cjnser535

Growing Community Sustenance: The Social Economy as a Route to Indigenous Food Sovereignty

2023· article· en· W4328119933 on OpenAlexaffvenueabout
Jennifer A. Sumner, JJ McMurtry, Derya Tarhan

Bibliographic record

VenueCanadian journal of nonprofit and social economy research · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsYork UniversityUniversity of Toronto
Fundersnot available
KeywordsFood sovereigntyIndigenousFoodwaysSovereigntySustenanceEconomyFood systemsPolitical economyPolitical scienceEconomic growthSociologyFood securityEconomicsGeographyEcologyLawAnthropologyAgriculture

Abstract

fetched live from OpenAlex

While the social economy can achieve many positive outcomes, one recent benefit is that it can be a route to Indigenous food sovereignty—a restorative framework for feeding communities and engaging in decolonization. This article examines how some Indigenous groups in Canada use the social economy to build food sovereignty, beginning with an overview of cultural relationships with food, its place in an Indigenous worldview, and the effect of colonization on Indigenous foodways. After introducing food sovereignty, and in particular Indigenous food sovereignty, it focuses on how some Indigenous communities are using the social economy to build food sovereignty, using the example of the Northern Manitoba Food, Culture, and Community Collaborative. The article concludes with a discussion of the importance of community and food sovereignty, not only for Indigenous Peoples but also for the social economy itself.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.365
Threshold uncertainty score0.735

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0190.034
Scholarly communication0.0070.004
Open science0.0010.010
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.053
GPT teacher head0.316
Teacher spread0.263 · 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

Citations2
Published2023
Admission routes3
Has abstractyes

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