Collaborating for (Game) Change(rs): Negotiating and Building Meaningful Action Research Partnerships
Bibliographic record
Abstract
Sport- and physical activity-related participatory action research (PAR) often involves voluntary partnerships spanning institutions, organizations, and jurisdictions. Negotiating and maintaining multi-stakeholder and multi-jurisdictional research partnerships can be likened to a delicate balancing act fraught with potential challenges and strains impacting project outcomes (e.g., waning commitment, emerging external factors, new and/or revised stakeholder/organizational requirements, fidelity to the necessary care given when working with community partners and participants). This article presents PAR as a methodology in sport and physical activity that can potentially engage all research participants as co-researchers, sharing power equitably. Recognizing the need for continued attention and action in this area, we provide an overview of PAR in practice, identifying significant ideas and principles. Additionally, we outline Game Changers—a PAR project involving students with various disabilities, schools, PE teachers, coaches, national and community sport partners, and university researchers. Based on lessons learned from this multi-stakeholder and multi-jurisdictional research project, we interrogate the possibilities associated with engaging in PAR by exploring challenges and opportunities related to sport and physical activity-focused PAR.
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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 | 0.149 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.020 | 0.034 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.007 | 0.039 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".