"Ogitchidaa", an Indigenous perspective on research sovereignty in Canada: Anishinaabe, Nēhinan (Cree), Saulteaux and Red River Métis voices from Manitoba and Saskatchewan
Bibliographic record
Abstract
There are many barriers Indigenous people and communities experience when trying to conduct research and access data that is about them. As Indigenous people work towards sovereignty in their governance and economics, having sovereignty in data and research is increasingly important as research and data are important for decision-making, funding, and policy development. Identifying what barriers exist, why they exist, and how Indigenous people can be supported to overcome them is how to achieve Indigenous Research Sovereignty. Individual Semi-structured interviews and a group discussion with Indigenous academics, students, community members, and those involved with Indigenous organizations was the qualitative (Western) method used to better understand the needs Indigenous communities have around data and research. Through Two-Eyed Seeing, Western Methods were informed by Indigenous methods of storytelling, testimonials, personal reflections, narratives, envisioning, and sharing circles to reflect the Indigenous focus of this research and ensure knowledge shared is presented appropriately. The barriers to Indigenous Research Sovereignty identified are hesitation and mistrust that stem from past experiences with research and ongoing impacts of colonialism; power dynamics and vulnerability; differing worldviews and research approaches; misunderstanding and miscommunication; pan-indigenizing and lack of representation; institutional barriers and systemic oppression; the reality that Indigenous needs around health and wellbeing are not being met; and, how there are not enough opportunities or resources for Indigenous people to conduct research. Supports to Indigenous Research Sovereignty are following Data Sovereignty and research principles; awareness, education, and training; inclusion, indigenization, and decolonization of institutions; celebrating cultural diversity; inclusion of culture and protocol in research; the use of Indigenous and community-led research; the recognition of Indigenous resurgence and promotion of Indigenous sovereignty; and the development of partnerships between allies, institutions, and communities. Truth and reconciliation are support for Indigenous Research Sovereignty because it is about supporting Indigenous people and addressing the barriers they face, by understanding the truths behind them. Indigenous people must be the ones leading the way for research and sovereignty, and they must be the ones who determine how they want to be included in colonial spaces.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.058 | 0.030 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| 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".