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Record W4410554425 · doi:10.1177/1356336x251341749

What's right with PE: Exploring positive narratives in physical education

2025· article· en· W4410554425 on OpenAlexaboutno aff
Shirley Gray, Karen Lambert, Lisa Young

Bibliographic record

VenueEuropean Physical Education Review · 2025
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsnot available
FundersMonash University
KeywordsPhysical educationNarrativePsychologyPedagogySociologyArt

Abstract

fetched live from OpenAlex

For decades, physical education (PE) scholars have called for a change to how PE is conceptualised and enacted, highlighting that its current (and persistent) form and focus – organised around physical activities and sports – are not fit for purpose. Much of this change-oriented discourse highlights what is wrong with PE, leading to suggestions about how PE should change in and for the future, for example, by adopting critical approaches and connecting more to the lives of young people. While we do not disagree with these perspectives and ideas, it is important to note that, in general, they have had little impact on PE curricula or pedagogy. In this paper, we suggest an alternative, strengths-based approach. Drawing from discussions with a range of professionals from the PE community (teachers, undergraduate and postgraduate pre-service teachers and teacher educators) across five national contexts – Canada, England, Ireland, Norway and Scotland – we generated narratives about ‘what's right with PE’. The narratives highlighted that PE can be ‘fit for purpose’ when it connects to the wider school and community, when everyone has a shared understanding of its purpose, and when PE teachers enact a broad, holistic and inclusive curriculum. We present the narratives as a reflective tool, encouraging all professionals within the PE community to consider how they align with (or against) their current experiences. We hope that these reflections facilitate critical thinking and problem solving to ensure that the subject is (and remains) fit for purpose now and in the future.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.752
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.083
GPT teacher head0.487
Teacher spread0.404 · 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.

Study designNot applicable
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 routes1
Has abstractyes

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