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Record W4386307226 · doi:10.18584/iipj.2023.14.2.14802

Anishinaabeg Elders’ Land-based Knowledge: Enacting Bagijigan for Health and Well-being

2023· article· en· W4386307226 on OpenAlexafffundvenue
Tricia McGuire-Adams

Bibliographic record

VenueInternational Indigenous Policy Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Ottawa
FundersUniversity of AlbertaCanadian Institutes of Health ResearchUniversity of Ottawa
KeywordsIndigenousTraditional knowledgeGeneral partnershipSociologyPublic relationsPolitical scienceGeographyEcologyLaw

Abstract

fetched live from OpenAlex

Indigenous Elders carry knowledge systems that are embedded within their respective land-based systems of knowledge. When Indigenous Elders pass away, their knowledge systems, if not preserved and documented, also pass away, which has lasting impacts on the continuance of Indigenous knowledge and practices of health and well-being. As a result of the enduring presence of settler colonialism, Indigenous Elders pass away at far earlier ages in comparison to their non-Indigenous counterparts. This article shows the results of an Indigenous health and well-being research project led by an Anishinaabe community in partnership with an Anishinaabe researcher. Guided by Anishinaabeg Elders and a Community Advisory Board, this research project preserves and documents Elders' knowledge of the land for community use and asks, how does knowledge of the land inform our health and well-being practices? In this article, we argue that Elders' knowledge is integral for regenerating critical well-being practices. We demonstrate that placing Elders' knowledge at the forefront of our well-being is an actionable practice of ganandawisiwin or good health. Without such knowledge and practices, we risk missing an opportunity to learn about well-being practices from our most precious knowledge holders.

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
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.973
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0130.000
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 head0.027
GPT teacher head0.396
Teacher spread0.369 · 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

Citations6
Published2023
Admission routes3
Has abstractyes

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