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Record W3212141800 · doi:10.33137/tijih.v1i2.36123

Strengthening Community through a Traditional Haudenosaunee Diet

2021· article· en· W3212141800 on OpenAlexaff
Kelly Gordon, A. Darlene Davis, Lori Davis Hill, Sujane Kandasamy, Bhavna Samtani, Nicole M. Bilodeau, Sonia S. Anand, Russell J. de Souza

Bibliographic record

VenueTurtle Island Journal of Indigenous Health · 2021
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsImpactMcMaster University
Fundersnot available
KeywordsIndigenousMedicineGerontologyPolitical science

Abstract

fetched live from OpenAlex

The Haudenosaunee Peoples of Eastern North America share a strong pastoral tradition and culture associated with maize agriculture. Traditional dietary practices were severely disrupted by colonial practices, and over time this has led to increased burdens of chronic diseases such as type 2 diabetes and obesity. To lead meaningful Traditional dietary practices, Haudenosaunee community members mobilized to develop and promote a 90-day challenge called ‘Healthy Roots.’ The Healthy Roots Program prioritizes Traditional Haudenosaunee foods found in territories during pre-contact times. The community decided to officially evaluate the impact of the dietary program using a pilot pre–post evaluation study (International Journal of Indigenous Health, 2021). We created the following digital story to capture the experiences and perspectives of the first cohort of community members to enroll in the Six Nations Healthy Roots Research Study. The film team attended six community meetings between February 2017 and October 2017 to capture the stories of participants before, during, and after the completion of the Healthy Roots Program. *Private Media Link for Video: https://bit.ly/3EJnsJO

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0110.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.095
GPT teacher head0.387
Teacher spread0.292 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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