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Record W2981437420 · doi:10.1093/cdn/nzz119

Community Champions for Safe, Sustainable, Traditional Food Systems

2019· article· en· W2981437420 on OpenAlexaboutno aff
Kathleen Yung, N Casey

Bibliographic record

VenueCurrent Developments in Nutrition · 2019
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
FundersUniversity of Minnesota
KeywordsChampionIndigenousFood securityTrainerPublic relationsBusinessPolitical scienceEconomic growthGeographyComputer scienceAgriculture

Abstract

fetched live from OpenAlex

Access to traditional Indigenous foods is a priority to improve food security and recognize the role of food in sustaining cultural and social connections. First Nations Health Authority (FNHA) is Canada's first province-wide, Indigenous-led health authority and delivers services in a community-driven manner. FNHA collaborated with First Nations to implement a Community Champion model, whereby each Nation could identify an individual who worked in food programming to attend a train-the-trainer workshop on safe food preservation methods. The Champions then took this knowledge, along with provided resources, to lead canning workshops in their home communities. Throughout the first year, a community of practice was nurtured, and a gathering of this community was held at the end of the first year. Nations were able to meet food safety considerations through interactive learning, and access to traditional Indigenous foods was strengthened. The Community Champion model supports capacity building and creates a community of practice.

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)0.011
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0130.012
Scholarly communication0.0080.006
Open science0.0030.016
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0270.003

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 head0.134
GPT teacher head0.389
Teacher spread0.255 · 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 designObservational
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

Citations11
Published2019
Admission routes1
Has abstractyes

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