The problematisation of ethnic and cultural diversity in physical education teacher education (PETE): an analysis of PETE course syllabi from Norway, Aotearoa/New Zealand and Canada
Bibliographic record
Abstract
An increasing number of studies have focused on policies in physical education and physical education teacher education (PETE).Policies are important because they prescribe behaviours or a course of action, and they legitimise some knowledge and perspectives while discrediting others.In the field of physical education (PE), a white, male-centric, middle-class, heteronormative and able-bodied understanding of bodies and sports has been the frame of reference for a long time, which can lead to the marginalisation, exclusion and alienation of pupils, students and faculty.In this paper, I investigate how ethnic and cultural diversity are represented in three PETE programmes in Norway, Aotearoa/New Zealand and Canada, respectively.Carol Bacchi's 'What's the Problem Represented to Be?' (WPR) approach for analysing policy was used to analyse course syllabi from the three selected PETE programmes.The findings suggest that ethnic and cultural diversity are represented in different ways, however, the course syllabi from all three countries were underpinned by liberal and critical approaches to multicultural education.This study has several implications.Teacher educators are reminded to pay attention to how the problematisation of ethnic and cultural diversity in course syllabi can allow or constrain different ways to think about these issues, and that policies can create certain types of 'problems' and 'subjects', which can lead to real-life consequences.Furthermore, they are reminded that these problematisations can be resisted and challenged.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".