Contemporary physical education curricula across Canada: an overview and analysis
Bibliographic record
Abstract
In Canada, the governance of K–12 public education is decentralised, yielding diverse educational policies and curricula across provinces/territories. Despite the benefits of localised curriculum development, the absence of a standardised national framework has resulted in varied educational policies and outcomes. To address this lack of uniformity, Physical and Health Education Canada (PHE Canada) recently introduced the Canadian Physical and Health Education Competencies (CPHE Competencies) to guide physical education (PE) curriculum reform. This paper presents an overview and descriptive analysis of PE curricula in Canada, assessing provincial/territorial PE curricula and their alignment with the CPHE Competencies. Our descriptive analysis revealed inconsistencies in curriculum structures, instructional time allocations, and graduation requirements among provinces/territories. Such disparities may underscore the need for a standardised approach to PE. Furthermore, our analysis identifies three critical considerations as a catalyst for deeper discussions: moving towards wholistic PE, the transition from adolescence to adulthood, and meeting the needs of the twenty-first century learner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.051 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".