Finding Bundles: Examining Health, Wellbeing, and Physical Education Through a Decolonial Lens
Bibliographic record
Abstract
The purpose of this study was to examine health, well-being, and physical education through a decolonial lens that focused on Indigenous worldviews, knowledges, and experiences. Utilizing a qualitative case study methodology, the goals of this project were to recognize how health and wellbeing (HWB) were understood by a small culturally diverse group of Indigenous Peoples located in the greater Tkaronto area. It additionally explored how these cultural understandings of HWB might support decolonized approaches to health and physical education (HPE) policies and curricula within York Region. The project embraced a variety of perspectives from diverse Indigenous students, educators, administrators, and Traditional Knowledge Keeper from two urban southern Ontario universities. Storytelling and thematic analysis were supported by decolonizing methodologies including the strength's perspective (Paraschak & Thompson, 2014) and two-eyed seeing offered by Albert and Murdena Marshall (Bartlett et al., 2012; Lavallée & Lévesque, 2013) that assisted in making visible how HWB were culturally understood. Indigenous Grounded Analysis (IGA) (Tuck & Gorlewski, 2016) and Traditional Indigenous Knowledges (TIK) (Maaka & Fleras, 2009) served as theoretical orientations that privileged and foregrounded Indigenous stories, knowledges, and experiences to consider how culturally diverse understandings of HWB might decolonize HPE within York Region.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.023 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| 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".