Teaching Yoga to Incarcerated Populations
Bibliographic record
Abstract
This chapter examines the policy and pedagogical implications of the global phenomenon of prison yoga, with a specific focus on community-taught yoga classes for men in Canada’s federal prison system. Situated in the literature on prison physical culture, broadly, and prison yoga, specifically, the chapter reviews international and Canadian policy landscapes and the pedagogical and organisational approaches of specific prison yoga organisations operating in Canadian corrections systems. The chapter includes an in-depth commentary from a community yoga teacher who taught incarcerated men for 10-years in Canada’s federal prison system, providing rich insights on the philosophy, pedagogical approach, challenges, and potential benefits of these programmes. This chapter finds that, despite its widespread presence in prisons around the globe and the potential benefits it provides to incarcerated people, yoga is not specifically integrated into corrections policies; and that, at least in Canada, prison yoga programmes are primarily provided on an ad hoc basis by community teachers and organisations. Further, the chapter demonstrates how sensitive, empathetic, and well-trained coaches can engage prisoners and give sport programmes added social significance for participants. The chapter provides insights to sport coaches working with incarcerated populations and academics studying sport and incarceration and includes recommendations for improving the delivery and impact of prison sport programmes.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".