Participatory Research in Sport and Physical Activity
Bibliographic record
Abstract
This is the first book to focus on participatory research in the context of sport and physical activity. It explores the transformative potential of participatory research methods and provides an introduction to the practicalities of doing participatory research in sport and physical activity. The book is structured around phases of the research process, covering research design, data collection, data analysis, and knowledge mobilization and translation. Chapters cover research design topics such as building research partnerships, reflexivity and ethical issues; methods such as social photo-elicitation, go-along interviews, and biographical mapping; analytical approaches such as collective memory work and collaborative analysis; and knowledge mobilization and translation topics such as podcasting, digital tools, and peer review. Every chapter includes a review of key developments, a guide to how that approach can be employed, an example from the author’s own work, and critical reflections on how that approach can shape future research and have an impact on public discourse. This book is an invaluable resource for students and researchers working in sport studies, the sociology of sport, sport and exercise psychology, sport management and policy, and human movement and health studies more broadly. It will be particularly useful for those interested in diverse and inclusive approaches to qualitative research methodologies.
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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 | 0.021 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".