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Record W3112086701 · doi:10.1093/bjc/azaa081

‘Keep Them on the Straight and Narrow’: Understanding, Selecting and Governing Subjects Through Intensive Supervision Units

2020· article· en· W3112086701 on OpenAlexafffundabout
Garrett Lecoq, Dale Ballucci, Dale Spencer

Bibliographic record

VenueThe British Journal of Criminology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsWestern UniversityCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsConfessionalPunishment (psychology)HarmPsychological interventionDisciplineCriminologySociologyCriminal justiceEconomic JusticePsychologyVulnerability (computing)Social psychologyLawPolitical scienceSocial scienceComputer securityComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Abstract Drawing from focus groups and semi-structured interviews, this paper examines decision-making practices and monitoring techniques of Canadian Intensive Supervision Units (ISUs) managing high-risk individuals in the community. We argue that ISU subjects are hyper-individualized through their unique conditions of release, contesting notions that actuarial risk assessments have eclipsed individual understandings of dangerousness in risk, correctional and policing literature. Using Foucault’s disciplinary, pastoral and confessional dispositifs, we highlight how ISU agents make subjects active participants in their own punishment. Moreover, we illustrate how dispositifs not only allow ISU agents to understand, select and govern subjects but also, more problematically, transform subjects into ostensibly dangerous entities reifying and necessitating escalating criminal justice interventions under auspices of protecting the community from potential—not guaranteed—harm.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.209
GPT teacher head0.308
Teacher spread0.098 · 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

Citations7
Published2020
Admission routes3
Has abstractyes

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