Whatuora: Theorizing "New" Indigenous Research Methodology from "Old" Indigenous Weaving Practice
Bibliographic record
Abstract
Despite Indigenous peoples’ deeply methodological and artistic ways of being in and making sense of our world, the notion of “methodology” has been captured by Western research paradigms and duly mystified. This article seeks to contribute to Indigenous scholarship that encourages researchers to look to our own artistic practices and ways of being in the world, theorizing our own methodologies for research from our knowledge systems to tell our stories and create “new” knowledge that will serve us in our current lived realities.I explain how I theorised a Māori [Indigenous peoples of Aotearoa New Zealand] weaving practice as a decolonizing research methodology for my doctoral research (Smith, 2017) to explore the lived experiences of eight Māori mothers and grandmothers as they wove storied Māori cloaks. I introduce you to key theoreticians who contributed significantly to my work so as to encourage other researchers to look for, and listen to, the wisdom contained within Indigenous knowledge and then consider the methodologies most capable of telling our stories from our own world-views.
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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 | 0.050 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.083 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| 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".