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Planning for Precarity? Experiencing the Carceral Continuum of Imprisonment and Reentry

2018· book-chapter· en· W2895149499 on OpenAlexaffabout
Gillian Balfour, Kelly Hannah‐Moffat, Sarah Turnbull

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of TorontoTrent University
Fundersnot available
KeywordsImprisonmentReentryPrisonScholarshipCriminologyPunishment (psychology)SociologyShadow (psychology)Political sciencePsychologySocial psychologyLaw

Abstract

fetched live from OpenAlex

Abstract Drawing on qualitative interviews with formerly imprisoned people in Canada, we show that most prisoners experience reentry into communities with little to no prerelease planning, and must rely upon their own resourcefulness to navigate fragmented social services and often informal supports. In this respect, our research findings contrast with much US punishment and society scholarship that highlights a complex shadow carceral state that extends the reach of incarceration into communities. Our participants expressed a critical analysis of the failure of the prison to address the needs of prisoners for release planning and supports in the community. Our findings concur with other empirical studies that demonstrate the enduring effects of the continuum of carceral violence witnessed and experienced by prisoners after release. Thus, reentry must be understood in relation to the conditions of confinement and the experience of incarceration itself. We conclude that punishment and society scholarship needs to attend to a nuanced understanding of prisoner reentry and connect reentry studies to a wider critique of the prison industrial complex, offering more empirical evidence of the failure of prisons.

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: Other · Consensus signal: Other
Teacher disagreement score0.290
Threshold uncertainty score0.565

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)0.0010.000

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 head0.042
GPT teacher head0.330
Teacher spread0.288 · 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
GenreOther

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

Citations23
Published2018
Admission routes2
Has abstractyes

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