Towards Haudenosaunee research sovereignty: Investing in local research and training to support community development
Bibliographic record
Abstract
Towards Haudenosaunee research sovereignty: Investing in local research and training to support community development The article emphasizes the importance of Indigenous Research Governance in Six Nations of the Grand River, addressing the harmful historical effects of academic research on Indigenous Peoples and advocating for structural changes that promote Indigenous data sovereignty and community ownership of research. In both Canada and the United States, academic research has long been part of the colonial project (Hodge, 2012; Williams et al., 2020). The impact research has had on Indigenous Peoples has resulted in a legacy of deep mistrust and negative perception of research by many Indigenous communities (Garrison et al., 2023). Indigenous scholars and leaders who have advocated for repairing this relationship have led major transformations away from the way in which research has traditionally been approached and administered. Most recent paradigm and policy shifts seek to support the establishment of self-determined Indigenous Research Governance (Garba et al., 2023; Morton et al., 2017), which encapsulates many interconnected key concepts, including Indigenous data sovereignty (Schnarch, 2004; Kukutai & Taylor, 2016; Cannon et al., 2024), Indigenous research ethics (Castellano, 2004; Kuhn et al., 2020; Fournier et al., 2023), Indigenous/ decolonizing methodologies (Kovach, 2009; Smith, 2021), and Indigenous epistemologies (McGregor et al., 2010; Karanja, 2019).
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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 | 0.026 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.033 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".