Decolonising physical literacy for human and planetary well-being
Bibliographic record
Abstract
Abstract Traditionally, physical education has focused on movement competency to develop skills for successful performance in different physical activities. Recently, however, the focus of many physical educators is shifting to notions of physical literacy to promote human flourishing through embodied experiences across multiple and diverse movement contexts well beyond physical education. While this shift is a welcome corrective to more traditional approaches to physical education, mainstream conceptions of physical literacy remain unduly narrow as rooted in colonial logics that continue to separate humans from the Earth while locating dominant categories of the human in hierarchical positions of power. In response, this article is an entanglement of Western and Métis embodiments of physical literacy. Deconstructing universalising models and modes of physical literacy set in dominant Western constructs, we seek to foster culturally relevant and meaningful physical literacy to promote physical activity and the wholistic health and well-being of Indigenous, or specifically, Red River Métis teachers and learners in Winnipeg, Canada. In doing so, we seek to provide a (re)visioning of human/Earth relationships as cultivated through movement-with Land; and thus, strengthen physical educational practices that more adequately attends to social (human) and ecological (Earth) flourishing in the context of global climate change.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".