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Developing an Innu framework for health research: The canoe trip as a metaphor for a collaborative approach centered on valuing Indigenous knowledges

2020· article· en· W3092386395 on OpenAlexafffundabout
Leonor M. Ward, Mary Janet Hill, Samia Chreim, Christine Poker, Anita Olsen Harper, Samantha Wells

Bibliographic record

VenueSocial Science & Medicine · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsCentre for Addiction and Mental HealthWestern UniversityPublic Health OntarioBell (Canada)University of TorontoAssembly of First NationsInstitute of Population and Public HealthUniversity of Ottawa
FundersInstitute of Aboriginal Peoples Health
KeywordsIndigenousCitizen journalismSociologyParticipatory action researchMetaphorTraditional knowledgeCommunity-based participatory researchEnvironmental ethicsGeographyPublic relationsPolitical scienceAnthropologyEcologyLaw

Abstract

fetched live from OpenAlex

Indigenous communities increasingly assert their right to self-determination by requiring that participatory research approaches be used, valuing and prioritizing Indigenous knowledges, for the purpose of improving Indigenous health. While frameworks that focus on Indigenous knowledges are being developed, these must be adapted or developed by Indigenous communities because their knowledge is specific to place and inherent to their lived experience. No community-based participatory research (CBPR) framework for health research has been developed with the Labrador Innu. In addition, while the literature emphasizes the importance of relationship in research with Indigenous communities, the process of establishing relationships is underspecified. Within this context, we describe our experience in developing a CBPR framework for health research in a study that is community-initiated and fitting within Innu self-determination. We highlight the importance of paying attention to the theoretical roots of CBPR, arguing that this helps researchers focus on the centrality of Indigenous knowledges (in this case, Innu knowledge). This requires that non-Indigenous researchers question assumptions of universality regarding their own knowledge and see all knowledges as equitable. Such posture of humility allows non-Indigenous researchers to enter relational spaces that join researchers and Indigenous communities. Within these spaces, a true collaborative approach is enabled and Indigenous knowledges are uncovered and become foundational in the inquiry process. We illustrate these ideas by describing a model for opening relational spaces that include Indigenous and non-Indigenous researchers. We then present a framework that uses the metaphor of canoeing together to capture our CBPR approach for use in Innu health research. We outline the behaviors of non-Indigenous researchers to build and solidify relationships with Indigenous community researchers over time. This article is useful for non-Indigenous researchers interested in relational approaches to research with Indigenous communities, and for Indigenous leaders and researchers who seek community solutions through research.

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)0.029
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0140.068
Scholarly communication0.0180.016
Open science0.0040.015
Research integrity0.0090.010
Insufficient payload (model declined to judge)0.0040.001

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 head0.294
GPT teacher head0.501
Teacher spread0.207 · 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.

Study designQualitative
DomainMethods
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

Citations22
Published2020
Admission routes3
Has abstractyes

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