Marking Physical Literacy or Missing the Mark on Physical Literacy? A Conceptual Critique of Canada’s Physical Literacy Assessment Instruments
Bibliographic record
Abstract
Margaret Whitehead first introduced the concept of physical literacy over 20 years ago. Since that introduction, physical literacy has been gaining in popularity within many Western physical education and sport contexts. This is particularly true within Canada, where physical literacy has been embraced by two of the nation’s most notable national physical education and sport organizations (i.e., Physical and Health Education Canada, Canadian Sport for Life). As physical literacy has been generating interest and action by these organizations, they, and others, have been quick to also seek methods by which to measure it. However, it is our observation that despite the promises and possibilities of physical literacy resources, initiatives, and programs, Canada’s most accessible physical literacy assessment instruments are wanting for focused and direct contemplation. In this article, we offer a conceptual critique of the physical literacy assessment instruments being developed for and practices being encouraged within Canadian school communities. Our contemplations consider three physical literacy assessment instruments, and they are focused, principally, upon usability, trustworthiness, and fidelity to Whitehead’s conception of physical literacy. We conclude that the instruments differ in their ease of use and usefulness, some are lacking, markedly, with respect to trustworthiness, and some fail to capture physical literacy as Whitehead intended it. Finally, in light of these conclusions, we offer suggestions for future practice and inquiry.
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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 | 0.046 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.022 | 0.098 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.001 | 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".