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Record W4396556670 · doi:10.1177/14624745241248153

Understanding carceral mobilities in and through lived experiences of incarceration

2024· article· en· W4396556670 on OpenAlexafffundabout
Sarah Turnbull, Dawn Moore

Bibliographic record

VenuePunishment & Society · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsCarleton UniversityUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMobilitiesMass incarcerationCriminologySociologyPrisonEconomic geographyGeographySocial science

Abstract

fetched live from OpenAlex

Recent scholarship on carceral mobilities critiques conceptualizations of carceral spaces as fixed and stable, and movements within or around sites of confinement as linear and horizontal. According to this critique, criminological studies of imprisonment have typically embraced what Turner and Peters (2017) [‘Rethinking mobility in criminology’, Punishment & Society 19(1), 96–114] term a ‘sedentarist ontology’ by failing to consider the complexities of prisoner mobilities in the lived experiences of the carceral. We draw on qualitative interview data from the Prison Transparency Project, a multiyear study initially across four research sites in Canada focused on former prisoners’ narratives of their carceral experiences, to identify and analyze the multifaceted mobilities that characterize prison life. We focus on three aspects of carceral mobilities: the use of psychotropic medications to produce docility, the coercive (im)mobilities of physical restraints and the ‘prison on wheels’ (i.e., prisoner transport vehicles). Using the concept of ‘kinetic immobility’, in which prisoners’ bodies are immobilized so they can be coercively moved (or not) through space and time, we consider the degree to which the theoretical work on carceral mobilities aligns with lived experiences of incarceration, as narrated by research participants.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.997

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 head0.112
GPT teacher head0.349
Teacher spread0.237 · 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 designQualitative
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

Citations5
Published2024
Admission routes3
Has abstractyes

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