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Record W3165825617 · doi:10.1177/17488958211017371

Dispositions that matter: Investigating criminalized women’s resettlement through their (trans)carceral habitus

2021· article· en· W3165825617 on OpenAlexafffundabout
Kaitlyn Quinn

Bibliographic record

VenueCriminology & Criminal Justice · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of TorontoUniversity of Alberta
KeywordsHabitusCriminologyHeterosexismSociologyGender studiesPsychologyPolitical scienceHomosexualityEthnographyAnthropology

Abstract

fetched live from OpenAlex

Whether prisoner resettlement is framed in terms of public health, safety, economic prudence, recidivism, social justice, or humanitarianism, it is difficult to overstate its importance. This article investigates women’s experiences exiting prison in Canada to deepen understandings of post-carceral trajectories and their implications. It combines feminist work on transcarceration and Bourdieusian theory with qualitative research undertaken in Canada to propose the (trans)carceral habitus as a theoretical innovation. This research illuminates the continuity of criminalized women’s marginalization before and beyond their imprisonment, the embodied nature of these experiences, and the adaptive dispositions that they have demonstrated and depended on throughout their lives. In doing so, this article extends criminological work on carceral habitus which has rarely considered the experiences of women. Implications for resettlement are discussed by tracing the impact of criminalized women’s (trans)carceral habitus (i.e. distrust, skepticism, vigilance about their environments and relationships) on their willingness to access support and services offered by resettlement organizations.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.106
GPT teacher head0.348
Teacher spread0.242 · 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.

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

Citations11
Published2021
Admission routes3
Has abstractyes

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