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Record W2147279908 · doi:10.1177/1462474505048132

Criminogenic needs and the transformative risk subject

2004· article· en· W2147279908 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePunishment & Society · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of Toronto
FundersU.S. Department of Justice
KeywordsRisk managementCriminal justiceTransformative learningSociologySituatedRisk assessmentPolitical riskRisk management toolsRisk societyCriminologyPoliticsEconomicsLawPolitical scienceSocial scienceComputer scienceFinanceManagement

Abstract

fetched live from OpenAlex

This article examines the discrepancies between theories of risk and penality and emergent strategies of risk/need identification and management. Working back from the strategies themselves, I argue that the current generations of risk/need technologies are a significant departure from the pessimistic theoretical accounts of risk in criminal justice associated with the ‘new penology’ and ‘actuarial justice’. I argue that risk knowledges are fluid and flexible and capable of supporting a range of penal strategies. The evolution and meanings of risk in correctional assessment and classification are examined to show how understandings of risk have shifted from static to dynamic categorizations. I show how the concept of need is fused with risk, how particular conceptions of ‘need’ and ‘risk’ are situated in local penal narratives, how need reconstructs risk and revives correctional treatment as an efficient risk minimization strategy. I argue that strategic alignment of risk with narrowly defined intervenable needs contributes to the production of a transformative risk subject who unlike the ‘ fixed or static risk subject’ is amenable to targeted therapeutic interventions. Newly formed risk/needs categorizations and subsequent management strategies give rise to a new politics of punishment, in which different risk/needs groupings compete for limited resources, discredit collective group claims to resources, redistribute responsibilities for risk/needs management and legitimate both inclusive and exclusionary penal strategies.

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.

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 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 score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.015
GPT teacher head0.274
Teacher spread0.259 · 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