Decolonizing Methodologies Through Dialogue: A Relational Literature Review on Urban Indigenous Health
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.103 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".