Conducting research “in a good way”: relationships as the foundation of research
Bibliographic record
Abstract
Indigenous Peoples across the world have a history of colonization that continues today. Additionally, Indigenous Peoples have experienced harm from research. This paper explores conducting research with Indigenous Peoples in a “good way”. Relationships built prior to and throughout the research process are foundational to conducting research in a good way, meaning the research respects and recognizes Indigenous inherent sovereignty; is culturally centered; relational; participatory; asset based; anti-racist; decolonizing; trauma-informed; survivor-centered; and engages free, prior, and informed, consent and Indigenous methodologies. This approach draws on the strength of Indigenous cultures, centering Indigenous Knowledges, and working toward Indigenous goals. A case study details the use of an Indigenous relational theoretical framework in practice, building life-long relationships through a research project that adapted a historically non-Indigenous methodology (ethnographic futures research) through a self-determining, participatory, and co-production project with the Ninilchik Village Tribe in Alaska. Our discussion broadens the application of this approach to research in any context with Indigenous children, youth, families, and Elders, reminding the reader that decolonization is not a metaphor but requires actual change in researchers, institutions, and funders.
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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 | 0.288 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.036 | 0.151 |
| Scholarly communication | 0.037 | 0.035 |
| Open science | 0.005 | 0.035 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".