Understanding the Application and Use of Indigenous Research Methodologies in the Social Sciences by Indigenous and Non-Indigenous Scholars
Bibliographic record
Abstract
Indigenous research methodologies articulate how researchers and Aboriginal communities engage in research together. These methodologies are informed by Indigenous cultural and ethical frameworks specific to the Nations with whom the research is being conducted. This study explores how such research relationships were articulated in the dissemination phase of research. We carried out an Indigenous qualitative content analysis of 79 peer-reviewed articles published January 1996 to June 2018, predominantly in the fields of social sciences. Our findings show that most articles were written by Indigenous researchers or a research team composed of Indigenous and non-Indigenous researchers. Such collaborations articulated the principles of Indigenous methodology (IM) much clearer than those authored by non-Indigenous scholars or when partnerships with Indigenous communities were less evident with respect to the principles guiding the research process. The principles of IM that were manifest in these research partnerships were relevance, respect for Indigenous knowledges, responsible relationships, wholism, and Indigenous ethics. The findings of this study will help to guide future researchers who work with Indigenous peoples, especially with respect to the need for a deeper understanding of how such research relationships are sustained over time to bring about meaningful change for Indigenous peoples and their communities.
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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 | 0.196 | 0.176 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.015 | 0.054 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| 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; 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".