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Record W2940367709 · doi:10.1177/1757975919831639

Examining Indigenous food sovereignty as a conceptual framework for health in two urban communities in Northern Ontario, Canada

2019· article· en· W2940367709 on OpenAlexafffundabout
Lana Ray, Kristin Burnett, Anita Cameron, Serena Joseph, Joseph LeBlanc, Barbara Parker, Angela Recollet, Catherine Sergerie

Bibliographic record

VenueGlobal Health Promotion · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsNOSM UniversityFirst Nations Health and Social Secretariat of ManitobaLakehead University
FundersInstitute of Aboriginal Peoples HealthMinistry of Natural Resources
KeywordsIndigenousFood sovereigntyNexus (standard)SovereigntyCorporate governanceFood systemsEconomic growthPolitical scienceGeographyFood securityBusinessPoliticsEcologyAgriculture

Abstract

fetched live from OpenAlex

While land is a nexus for culture, identity, governance, and health, as a concept land is rarely addressed in conversations and policy decisions about Indigenous health and well-being. Indigenous food sovereignty, a concept which embodies Indigenous peoples' ability to control their food systems, including markets, production modes, cultures and environments, has received little attention as a framework to approach Indigenous health especially for Indigenous people living in urban spaces. Instead, discussions about Indigenous food sovereignty have largely focused on global and remote and rural communities. Addressing this gap in the literature, this article presents exploratory work conducted with Waasegiizhig Nanaandawe'iyewigamig and Shkagamik-Kwe Health Centre, two Indigenous-led Aboriginal Health Access Centres in urban service centers located in Northern Ontario, Canada.

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)0.003
metaresearch head score (Gemma)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.968

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0350.012
Scholarly communication0.0050.001
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.351
Teacher spread0.312 · 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

Citations40
Published2019
Admission routes3
Has abstractyes

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