Exploring the Effectiveness of Métis Women’s Research Methodology and Methods: Promising Wellness Research Practices
Bibliographic record
Abstract
In this article, we share our experience conducting research with Métis women as Métis women researchers. We engaged in promising research practices through visiting, ceremony, and creative methods of art and writing embedded in what we identify as a learning-by-doing practice. Through collaborative and Indigenous relational methodology, we sought to support a culturally safe, nurturing space where Métis women could learn from one another and express Métis knowledge about the specific roles and responsibilities of Métis Aunties within our respective kinship system. This inquiry into the roles of Métis Aunties included a creative art and writing dialogue event in the Métis river community of St. Louis in Saskatchewan, attended by women who were Métis Aunties or nieces. The purpose of the event was to learn more about our Métis Aunties, building on Dr. Kim Anderson’s (2016) extensive research on women’s roles in the governance, care, and wellness of our healthy/balanced kinship systems. We chose this specific region because of its historical significance to Métis people as a river place, and our own personal connections to Métis families in this area. We share our processes in learning with and from other Métis women in order to contribute to the growing literature on relational approaches to research.
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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 | 0.584 | 0.435 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.018 | 0.038 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".