Exploring collective action in becoming a teacher in physical education: Understanding the development and use of signature pedagogies across teacher education contexts
Bibliographic record
Abstract
Researchers have highlighted the urgent need for large-scale international collaborative research projects between teacher education and school physical education (PE) to develop practices and understandings that address the grand challenges facing the field ( MacPhail and Lawson, 2021 ). In response, this article outlines and illustrates the design and methodology of an international project built on collaboration among PE teacher educators, in-service teachers, and pre-service teachers (PSTs). This collaborative work aimed to explore the development and use of signature pedagogies as collective action across diverse PE teacher education contexts, including both initial teacher education and continuous professional development in five European countries. This article serves two purposes. First, it presents a design for international collaborative research between school PE and teacher education, with a specific focus on signature pedagogies in PE teacher education. Second, it illustrates the methodological approach, detailing the research methods used to explore signature pedagogies across varied international contexts. In so doing, the article contributes to the field by offering a framework for designing international research that engages with collective action and pedagogical innovation. We advocate for research designs that employ robust methodologies, clearly defined analytical frameworks, and transparent procedures. Such designs are essential for conducting large-scale international collective action projects involving teacher educators, in-service teachers, and PSTs from diverse PE teacher education contexts. We argue that these elements are critical for scaling up research in the field and for supporting the development, adaptation, and use of signature pedagogies across educational settings.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.015 |
| 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".