Awakening Movement Consciousness in the Physical Landscapes of Literacy: Leaving, Reading and Being Moved by One’s Trace
Bibliographic record
Abstract
Physical literacy, a concept introduced by Britain’s physical education and phenomenological scholar, Margaret Whitehead, who aligned the term with her monist view of the human condition and emphasis that we are essentially embodied beings in-the-world, is a foundational hub of recent physical education curricular revision. The adoption of the term serves a political purpose as it helps stakeholders advocate for the educational, specifically literacy, rights of the whole child. Yet, one might wonder what impact conceptual shifts of becoming “physically literate” in lieu of becoming “physically educated” have on physical education research and practice. Terms such as “reading” the game and metaphors that describe the body as an “instrument of expression” are entering the lexicon of physical education but from a seemingly cognitive frame of reference. Arguably, the extent to which the adoption of physical literacy has on dissolving Cartesian views of the body and the mechanization of movement it performs has yet to be questioned. This article thus acts as an invitation to explore physical literacy in a Merleau-Pontian inspired act of inscribing the world through movement and how a reading of a reversible imprint might awaken a more fluent sense of what it means to become physically literate as new curricular pathways in the field of physical education emerge.
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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 | 0.005 | 0.055 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".