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Record W2312599220 · doi:10.5304/jafscd.2013.033.012

Northern Food Networks: Building Collaborative Efforts for Food Security in Remote Canadian Aboriginal Communities

2013· article· en· W2312599220 on OpenAlexaffabout
Rebecca Schiff, Fern Brunger

Bibliographic record

VenueJournal of Agriculture Food Systems and Community Development · 2013
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsFood securityFood systemsGeographyFood insecurityDiversity (politics)Environmental planningBusinessFood processingEnvironmental resource managementConsumption (sociology)Environmental protectionPolitical scienceAgricultureSociologyEnvironmental science

Abstract

fetched live from OpenAlex

Canada's northern and remote regions experience high rates of food insecurity, exceptionally high food costs, environmental concerns related to contamination and climate change, and a diversity of other uniquely northern challenges related to food production, acquisition, and consumption. As such, there is a need to understand and develop strategies to address food-related concerns in the North. The diversity of communities across the North demands the tailoring of specific, local-level responses to meet diverse needs. Over the past decade, local networks have emerged as a powerful method for developing localized responses, promoting food security and the development of more sustainable food systems across Canada and North America. Despite this, there is a paucity of research examining challenges and effective approaches utilized by these local networks or their potential applicability for building food security in rural, remote, and northern communities. This research utilized participant observation as a method to examine the experiences of a Northern Canadian food security network. The experience of this network points to strategies that can lead to successful collaborative approaches aimed at implementing programs to address food security in northern and remote 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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.817
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0070.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.021
GPT teacher head0.293
Teacher spread0.272 · 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.

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

Citations10
Published2013
Admission routes2
Has abstractyes

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