Challenges of Non-Indigenous Researchers in Indigenous Contexts
Bibliographic record
Abstract
Indigenous peoples have been historically othered by non-Indigenous researchers who used to devalue their ways of being, undermine their worldviews, and send their children to residential schools to be deculturated from their Indigeneity. As a result, a mistrust has been created among Indigenous communities towards non-Indigenous research and its lack of consent from their participants. This, however, has been changing, and researchers increasingly explore other ways of knowing in higher education and establish their research from the lens of their Indigenous participants. Although it is the right path to take, there are challenges that non-Indigenous scholars encounter when they conduct research in such contexts. This paper aims to highlight such challenges and groups them under research methodologies, worldviews, and participants in the hopes of helping like-minded researchers keen on exploring other ways of knowing.
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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 | 0.308 | 0.293 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.055 | 0.071 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.009 | 0.037 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".