How do we teach about ethnic and cultural diversity in PETE? A discourse analysis of interviews from Norway, Aotearoa/New Zealand and Canada
Bibliographic record
Abstract
Background Although social justice has been a topic of interest in physical education for over 40 years, research still shows that physical education excludes and marginalises students based on various identity dimensions. Presently, with societies becoming more diverse, there is heightened anticipation for teacher educators to demonstrate ‘cultural responsiveness’ or ‘cultural competence’.Purpose This paper investigates how Physical Education Teacher Education (PETE) educators in Norway, Aotearoa/New Zealand and Canada construct teaching about ethnic and cultural diversity.Methods This study’s data comprise 23 interviews with PETE educators from three countries. We analysed the interviews using concepts from discourse theory and a framework from multicultural education.Findings While the way teacher educators discuss teaching about issues of ethnic and cultural diversity in PETE highly depends on each country’s contexts, there still are overlaps encapsulated by discourses. Our analysis suggests most discursive articulations from interviews reproduced liberal discourses, with fewer discursive articulations from conservative and critical discourses. Furthermore, the discourses constructed certain subject positions for students and teacher educators that carry implications for practice.Conclusion Although there are consistent attempts to engage with critical discourses, the dominance of liberal discourses persists in PETE in all three countries. This prevalence poses a risk of perpetuating othering and marginalisation.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".