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Record W4409591542 · doi:10.1080/25742981.2025.2486622

Contemporary physical education curricula across Canada: an overview and analysis

2025· article· en· W4409591542 on OpenAlexafffundabout
Lauren Sulz, Hayley Morrison, Daniel B. Robinson, Joe Barrett

Bibliographic record

VenueCurriculum Studies in Health and Physical Education · 2025
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsSt. Francis Xavier UniversityBrock UniversityUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCurriculumPhysical educationEngineering ethicsSociologyPedagogyEngineering

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.167
GPT teacher head0.588
Teacher spread0.422 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes3
Has abstractyes

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