Exploring Future Research in Physical Education: Espousing a Social Justice Perspective
Bibliographic record
Abstract
Although there has been forward movement in identifying and addressing diverse learning needs, social justice education is not a significant part of the current standards for beginning teachers or K-12 students in the U.S. Throughout our standards-based history, social justice has been more of a hidden curriculum. To attain the 50 Million Strong by 2029 goal, it is vital to acknowledge that physical education is a social justice issue. Without consideration of the historical, political, and social contexts that permeate and frame physical education, along with the social identities and lived experiences of our future teachers and students, it is unlikely that this goal will be sustained. While concerns have been voiced relative to the standards-based teaching movement, in a country that espouses standards-based education, a first step in moving any educational reform forward is to formalize its inclusion in the national standards that serve to guide our discipline. A philosophical shift may be what is needed for change to occur regarding social justice education in an attempt to enhance the learning opportunities for all students. A forward step in creating this change is to address the research and pedagogical practices of our current physical education teacher education and K-12 programs, along with the physical education standards and policies at the national and state levels. We specifically articulate connections between social justice education and four key, interconnected research areas related to (a) occupational socialization, (b) curriculum, instruction, and assessment, (c) technology, and (d) professional development.
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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 | 0.073 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.020 | 0.117 |
| Scholarly communication | 0.031 | 0.038 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".