Reimagining teaching personal and social responsibility (TPSR) within health and physical education curriculum: exploring the transformative potential of the socially-just TPSR approach
Bibliographic record
Abstract
This Special Issue re-imagines Hellison's Teaching Personal and Social Responsibility (TPSR) as a viable pedagogy for social justice. We bring together international scholars to explore the opportunities, challenges and transformative potential of TPSR and the Socially-Just TPSR (SJ-TPSR) approach within health and physical education/teacher education. The papers provide historical context for TPSR's connection to social justice, offer empirical insights from diverse international contexts (Spain, Canada, Greece) and provide an autoethnographic account of a teacher's journey toward critical pedagogy (Indonesia). Conceptual papers, comparing TPSR to other models and reinterpreting it through a Freirean lens, are presented. The Special Issue also includes an international collaborative self-study (Australia, Canada and Ireland), which constructed guiding principles for the SJ-TPSR approach. Together, these contributions advocate for a dynamic, equity-oriented framework, offering the SJ-TPSR approach as a clear, actionable model for educators and researchers seeking to explicitly integrate social justice into their teaching and research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".