Indigenous communities, collectives and organizations advancing decolonizing methodologies: perspectives from British Columbia Network Environment for Indigenous Health Research in Canada
Bibliographic record
Abstract
Purpose The British Columbia Network Environment for Indigenous Health Research (BC NEIHR) aims to support and advance research leadership among Indigenous communities, collectives and organizations (ICCOs) within British Columbia, Canada. The BC NEIHR provides support and funding to ICCOs for research development and knowledge sharing. This funding model supports ICCOs' self-determined health research by providing funds that are fully controlled by ICCOs, without the requirement of a non-Indigenous host organization. Design/methodology/approach We conducted a critical analysis of 35 ICCO research development and knowledge-sharing grant applications to identify how ICCOs are decolonizing research and methodologies. Findings Six themes were identified from ICCO decolonizing methodologies: (1) identified, driven, and led by Indigenous Peoples and community; (2) guidance from advisors, ethical guidelines, and local protocols; (3) follow traditional and cultural practices; (4) determine what is knowledge and ways to share knowledge; (5) celebrating the sharing and returning of knowledge and (6) advancing relationality: building and strengthening relationships. Originality/value This paper highlights the impact of how the BC NEIHR and ICCOs are advancing decolonizing methodologies to support self-determined Indigenous health research led by, and grounded in, Indigenous communities. It reflects on the work of Maori scholar Linda Tuhiwai Smith’s Decolonizing Methodologies: Research and Indigenous Peoples and contributes to the literature of decolonizing methodologies.
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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 | 0.033 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.084 | 0.037 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".