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Record W4405113759 · doi:10.1177/10778004241298923

Decolonizing Methodologies Through Dialogue: A Relational Literature Review on Urban Indigenous Health

2024· article· en· W4405113759 on OpenAlexafffund
Gabrielle Legault, Kelsey Darnay, Shawn Wilson, Peter Hutchinson

Bibliographic record

VenueQualitative Inquiry · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsIndigenousScholarshipSociologyDisciplineCommunity engagementTraditional knowledgeHonorEngineering ethicsSocial sciencePolitical sciencePublic relationsEcology

Abstract

fetched live from OpenAlex

This article responds to Tynan and Bishop’s call for relational engagement in literature reviews, employing Indigenous epistemologies and a relational, conversational approach to examine trends in urban Indigenous health research. Reflecting on a comprehensive literature review, we highlight shifts in research focus, authorship, and methodologies. The analysis, guided by Indigenous scholars with diverse disciplinary backgrounds, centers on current and emerging trends, while addressing the impact of funding priorities and sociopolitical contexts on research. Despite growing recognition of Indigenous scholarship and community engagement, our discussions reveal challenges in maintaining authenticity in community-driven methodologies. We emphasize the need to further decolonize research approaches, highlighting the role of relational methodologies that honor Indigenous epistemologies. By integrating oral traditions, community-based sources, and relational citation practices, this article advocates for a paradigm shift in literature reviews, urging scholars to prioritize Indigenous ways of knowing and strengthen the relational dynamics within research processes to better address urban Indigenous health and well-being.

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)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.861
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.259
GPT teacher head0.523
Teacher spread0.264 · 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
GenreReview

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

Citations3
Published2024
Admission routes2
Has abstractyes

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