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Record W2971005395 · doi:10.5070/r341042359

Transforming Space? Spatial Implications of Yoga in Prisons and Other Carceral Sites

2019· article· en· W2971005395 on OpenAlexaffabout
Mark Norman

Bibliographic record

VenueRace and Yoga · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTransformative learningSociologyPrisonGlobeSpace (punctuation)Field (mathematics)Perspective (graphical)Social scienceCriminologyEpistemologyPsychologyPedagogy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.308
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2019
Admission routes2
Has abstractyes

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