Teaching Social Justice Through TPSR: Where Do I Start?
Bibliographic record
Abstract
In this paper we offer practical suggestions for integrating social justice content into physical-activity based physical education, namely, through a socially-just TPSR approach. We first address the challenges of using pedagogies for social justice in physical education. This is followed by a brief overview of TPSR (the what) and a re-imagined TPSR approach from a social justice lens. Next, practical examples for developing a socially-just TPSR approach are offered such as ways to a) examine and practice socially just behaviors, b) include transfer and advocacy, and c) use a five-part TPSR lesson plan to integrate social justice issues into physical-activity-based physical education settings. Final thoughts include a) being patient and kind to yourself when implementing unfamiliar approaches and issues, and b) making decisions about the inclusion of social justice issues based on what’s personally meaningful for students as well as context, and c) using a TPSR approach to enact social justice content requires a reconceptualization of the model through a social justice lens.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".