Wiisokotaatiwin: an Anishinaabe research methodology to advance physical activity and health perspectives from Indigenous women in Canada
Bibliographic record
Abstract
Indigenous women’s perspectives about and participation in physical activity are needed. For too long, mainstream ideas about how Indigenous women should address their health and well-being have permeated solutions to such issues as chronic diseases and sedentarism, among other ill health factors. As a result, Indigenous women are often underrepresented as experts in their own movement and physical activity goals. Yet, their perspectives on physical activity are crucial for informing the development of specific programs, policies, and engagement with physical activity. Thus, this Anishinaabe-led study brings attention to the multiple perspectives urban Indigenous women have about their health and physical activity. 12 Indigenous women participated in a study employing an Anishinaabe research methodology, Wiiskotaatiwin, meaning gathering together for a purpose. A series of critical dialogues on health and related topics (e.g. decolonisation and health, the importance of physical activity) was employed to address the research questions. The women also participated in physical activity prior to each dialogue, thereby creating a novel research approach. The findings are important because physical activity is rarely the focus of Indigenous-led research among Indigenous women aimed at amplifying their health resurgence. The results of this study amplify Indigenous women’s perspectives on physical activity and its connections to decolonisation and the disruption of colonialism.
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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 | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.026 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".