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Record W2305435634 · doi:10.1093/heapro/daw013

Exploring and revitalizing Indigenous food networks in Saskatchewan, Canada, as a way to improve food security

2016· article· en· W2305435634 on OpenAlexaffabout
Fidji Gendron, Anna Hancherow, Ashley Norton

Bibliographic record

VenueHealth Promotion International · 2016
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsFirst Nations University of Canada
Fundersnot available
KeywordsIndigenousFood securityLikert scaleFood insecurityQualitative researchTraditional knowledgePublic relationsQualitative propertyPolitical scienceEconomic growthSocioeconomicsGeographySociologyPsychologySocial scienceAgriculture

Abstract

fetched live from OpenAlex

The project discussed in this paper was designed to expand research and instigate revitalization of Indigenous food networks in Saskatchewan, Canada, by exploring the current state of local Indigenous food networks, creating a Facebook page, organizing volunteer opportunities and surveying workshop participants regarding their knowledge and interest in Indigenous foods. The survey included Likert scale questions and qualitative questions. Project activities and survey results are discussed using statistical and qualitative analysis of the themes. Results indicate that participants are very interested in learning more about, and having greater access to, traditional foods and suggest that supporting Indigenous food networks may be an appropriate response to food insecurity in communities. Elders and community members are vital players in Indigenous foods exploration and revitalization in Saskatchewan by passing on traditional education.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.251

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0150.004
Scholarly communication0.0030.001
Open science0.0010.005
Research integrity0.0000.001
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 head0.077
GPT teacher head0.353
Teacher spread0.276 · 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

Citations20
Published2016
Admission routes2
Has abstractyes

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