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Record W335030047 · doi:10.5204/ijcis.v7i1.118

Maintaining the Integrity of Indigenous Knowledge; Sharing Metis Knowing Through Mixed Methods

2014· article· en· W335030047 on OpenAlexaffabout
Peter Hutchinson, Carlene Dingwall, Donna Kurtz, Mike Evans, Gareth R. Jones, Jon Corbett

Bibliographic record

VenueInternational Journal of Critical Indigenous Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsMetisIndigenousParticipatory action researchTraditional knowledgeCitizen journalismPublic relationsDisseminationPopulationPolitical scienceSociologyKnowledge managementComputer scienceEcology

Abstract

fetched live from OpenAlex

Working collaboratively with Indigenous populations necessitates a focus on partnerships at the core of sharing, implementing and disseminating Indigenous knowledge. The Tri-Council Policy 2 ISSN: ISSN 1837-0144 © International Journal of Critical Indigenous Studies Statement (CIHR, 2010) notes that respectful, reciprocal and ethical research standards must be applied to research with Indigenous communities. Métis collaborators identified that relationships must be regarded as the central focus of sharing Metis knowledge. Utilizing an investigation on the health benefits of participating in cultural activities, specifically harvesting, we demonstrate how applying mixed methods meets and informs these research standards and creates a unique, participatory Indigenous research method relevant for Métis people. Building from these research standards, this collaboration developed a method of investigation that shares Indigenous knowledge of population health. This method promotes a sustainable research relationship, moving beyond fragmented research projects and making relational connections between people, data sources and findings

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)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.086
GPT teacher head0.489
Teacher spread0.403 · 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

Citations10
Published2014
Admission routes2
Has abstractyes

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