What's Right with PE: Exploring Positive Narratives in Physical Education
Bibliographic record
Abstract
The dataset is a series of 5 narratives developed from discussions between PE practitioners from Scotland, England, Ireland, Canada and Norway. Their discussions were around ‘what’s right’ and ‘what’s wrong’ with PE. Full details of the analysis process can be found in the following article (just accepted for publication). Gray, S., Lambert, K. & Young, L. (accepted April 2025). What's Right with PE: Exploring Positive Narratives in Physical Education. European Physical Education Review. 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 sport - 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".