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Record W2562444937 · doi:10.15353/cfs-rcea.v3i2.161

Land-Based programs in the Northwest Territories: Building Indigenous food security and well-being from the ground up

2016· article· en· W2562444937 on OpenAlexafffundvenueabout
Sonia Wesche, Meagan Ann O’Hare-Gordon, Michael A. Robidoux, Courtney W. Mason

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2016
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsThompson Rivers UniversityUniversity of Ottawa
FundersPartenariat Canadien Contre Le CancerAurora Research InstituteUniversity of Ottawa
KeywordsIndigenousFood securityFood sovereigntyProcurementContext (archaeology)FoodwaysGeographyFood systemsFood insecurityEnvironmental planningEconomic growthBusinessEnvironmental protectionPolitical scienceEnvironmental resource managementEcologyAgricultureSociologyEconomicsMarketingBiology

Abstract

fetched live from OpenAlex

Food security in Canada’s North is complex, and there is no singular solution. We argue that land-based wild food programs are useful and effective in contributing to long-term food security, health and well-being for Indigenous communities in the context of changing environmental conditions. Such bottom-up programs support cultural continuity and the persistence of skills and knowledge that, over time, increase local food security and food sovereignty. This paper (a) highlights the link between observed environmental changes and wild food procurement in two Indigenous communities in the Northwest Territories, (b) compares and discusses the impacts of two collaboratively developed, community-based programs to improve foodways transmission and capacity for wild food procurement, and (c) identifies lessons learned and productive ways forward for those leading similar efforts in other Indigenous 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 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)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.193

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0120.004
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.048
GPT teacher head0.295
Teacher spread0.247 · 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

Citations35
Published2016
Admission routes4
Has abstractyes

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Same venueCanadian Food Studies / La Revue canadienne des études sur l alimentationSame topicIndigenous Studies and EcologyFrench-language works237,207