The Significance of a Relations-based Approach to Indigenous Research Ethics and Indigenous Data Sovereignty
Bibliographic record
Abstract
BACKGROUND: Indigenous people have been increasingly asserting self-determination in research to “research ourselves back to life”. There is a current knowledge gap regarding how gender is considered in Indigenous research ethics and its implications for Indigenous self-determination in research. METHODS: Utilizing critical discourse analysis and a decolonizing theoretical framework a systematic review was conducted to contribute to filling this knowledge gap. RESULTS: The dominant concept and language of gender as binary are being used in Indigenous research conducted in observance of Indigenous research and it is given significance through its continued use, particularly in relation to participant sampling and bias. The mainstream concept is also given significance because research involving Indigenous people is in response to inequities resulting from colonization. However, there is resistance to this concept and its significance by revitalizing and renewing Indigenous Ways of Knowing (research paradigms including epistemology, methodology, methods, and theories) such as language and most significantly, elevating relations (human-to-human and human-to-nature) as part of Indigenous Ways of Being (ontology). The implications of this recovery and renewal is alignment and strengthening of Indigenous Data Sovereignty. This is ethical Indigenous research. CONCLUSION: “Researching ourselves back to life” involves going back to the very beginning, to our very being as Indigenous peoples and relating this to how we understand, conduct, and utilize ethical research to express and reflect our reality for wellness, governance, and nation-(re)building.
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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 | 0.300 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.015 | 0.125 |
| Scholarly communication | 0.023 | 0.028 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".