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Record W2465956937 · doi:10.18357/ijih111201616016

Feasting for Change: Reconnecting with Food, Place & Culture

2016· article· en· W2465956937 on OpenAlexaffvenueabout
Jen Bagelman, Fiona Deveraux, Raven Hartley

Bibliographic record

VenueInternational Journal of Indigenous Health · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsIsland HealthUniversity of Victoria
Fundersnot available
KeywordsIndigenousMainstreamExperiential learningTraditional knowledgeValue (mathematics)SociologyPower (physics)Community developmentPublic relationsEnvironmental ethicsPolitical sciencePedagogyEcologyLaw

Abstract

fetched live from OpenAlex

This paper examines and shares the promising practices that emerged from an innovative project, entitled “Feasting for Change,” in promoting health and well-being. Taking place on Coast Salish territories, British Columbia, Canada, Feasting for Change aimed to empower Indigenous communities to revitalize traditional knowledge about the healing power of foods. This paper contributes to a growing body of literature that illuminates how solidarities between Indigenous and non-Indigenous communities can be fostered to support meaningful decolonization of mainstream health practices and discourses. In particular, it provides a hopeful model for how community-based projects can take inspiration and continual leadership from Indigenous Peoples. This paper offers experiential and holistic methods that enhance the capacity for intergenerational, land-based, and hands-on learning about the value of traditional food and cultural practices. It also demonstrates how resources (digital stories, plant knowledge cards, celebration cookbooks, and language videos) can be successfully developed with and used by community to ensure the ongoing process of healthful revitalization.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.981
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.059
GPT teacher head0.374
Teacher spread0.315 · 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 designNot applicable
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
Published2016
Admission routes3
Has abstractyes

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