Transforming Space? Spatial Implications of Yoga in Prisons and Other Carceral Sites
Bibliographic record
Abstract
Yoga programs have taken root, and in some cases flourished, in correctional institutions across the globe, yet few scholars have examined this phenomenon from critical theoretical and qualitative perspectives. The goals of this paper are to explicitly link scholarly discussions of yoga in prisons with theoretical developments in criminology, sociology, and human geography; and to use these diverse perspectives to develop a theoretical understanding of the possibilities and limits of yoga as a transformative spatial practice in carceral settings. Drawing on qualitative data collected on prison yoga, primarily in Canada, this paper considers three lines of theoretical inquiry. Firstly, it examines yoga classes as an “institutional display” that facilitates social interaction between prisoners and community members, yet also serves administrative interests. Secondly, it considers the possibilities for yoga spaces to enable forms of emotional expression that may not be permitted in other areas of the institution. And thirdly, it discusses the implications of yoga in carceral spaces beyond prisons. The paper draws heavily on the emergent field of carceral geography, as well as sociological and criminological research, to advance these arguments. In presenting these theoretical analyses, this paper advocates for a deeper theoretical exploration of the multiplicity of spatial meanings of yoga in carceral settings.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.045 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".