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Record W4416331716 · doi:10.1177/14624745251396554

‘It's an awful thing, but it's better than being dead’: Exploring the emotional circuitry of failed segregation reforms in Canadian prisons

2025· article· en· W4416331716 on OpenAlexafffundabout
Sophie Lachapelle, Jennifer M. Kilty

Bibliographic record

VenuePunishment & Society · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPunitive damagesRhetoricGovernment (linguistics)FalsityIntervention (counseling)Prison

Abstract

fetched live from OpenAlex

Prison authorities around the world are increasingly adopting more politically palatable alternatives to segregation - often referred to as solitary confinement. In 2019, the Canadian government enacted Bill C-83, legally abolishing segregation in federal prisons - at least, on paper - and established Structured Intervention Units (SIUs) as a supposedly less harmful alternative. Critics have problematized the Correctional Service of Canada's implementation of the SIU model as maintaining previous segregation practices under the rhetoric of 'reform'. Building upon these criticisms, this article challenges the common refrain that segregation spaces are prisons within prisons, spaces that are somehow different - extraordinary even - in terms of their material conditions and administration. While segregation or SIUs reflect the most austere and punitive form of isolation, they do not exist apart from the wider carceral environment. Developing the notion of emotional circuitry, we examine formerly incarcerated people's (n = 57) experiences of moving between general population, SIUs, and other segregation-like spaces. We highlight the falsity of distinguishing SIUs as a distinct carceral space, and the importance of understanding its spatial, material, and emotional effects as born from the violence of the broader incarceration experience that no degree of segregation-specific reforms can ameliorate.

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)0.000
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 score0.279
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.031
GPT teacher head0.298
Teacher spread0.268 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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