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Record W2807326681 · doi:10.15353/cfs-rcea.v5i2.210

Healthy Roots: Building capacity through shared stories rooted in Haudenosaunee knowledge to promote Indigenous foodways and well-being

2018· article· en· W2807326681 on OpenAlexaffvenueabout
Kelly Gordon, Adrianne Lickers Xavier, Hannah Tait Neufeld

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2018
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of GuelphRoyal Roads University
Fundersnot available
KeywordsFoodwaysSustenanceIndigenousTraditional knowledgeFood systemsEconomic growthSociologyFood securityEcologyAgricultureBiologyAnthropologyEconomics

Abstract

fetched live from OpenAlex

Urban and reserve-based First Nation families in southern Ontario frequently experience food insecurity as well as more limited access to traditional, more nutrient dense foods from the local environment. Healthy Roots was initiated in the community of Six Nations to promote traditional food consumption. A small number of participants eating only locally available foods reported better-controlled blood glucose, positive weight change and increased traditional food knowledge. New relationships and partnerships were also developed. Our Sustenance, a community organization that was responsible for the local farmers market, community gardens, good food box program, and other community programs, joined the Healthy Roots Committee to continue advancing the knowledge and activation of the community-based initiatives such as the development of a Haudenosaunee Food Guide. Healthy Roots may serve as a model and inspiration to other Indigenous communities looking to reconnect to their local environments and Indigenous lifeways to promote Indigenous foodways and well-being.

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)0.006
metaresearch head score (Gemma)0.006
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 score0.921
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.014
Scholarly communication0.0050.004
Open science0.0020.013
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.072
GPT teacher head0.350
Teacher spread0.278 · 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

Citations17
Published2018
Admission routes3
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