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Record W2149941162 · doi:10.1177/14668025030033001

`Doing Good with a Vengeance':

2003· article· en· W2149941162 on OpenAlexafffundabout
Benedikt Fischer

Bibliographic record

VenueCriminal Justice · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersCanadian Institutes of Health Research
KeywordsPunitive damagesCriminologyCriminal justiceTherapeutic jurisprudenceRecidivismPoliticsLegitimacyDeviance (statistics)Political scienceDrug courtSocial controlPopularityLawSociologyPsychologyPsychiatry

Abstract

fetched live from OpenAlex

`Drug treatment courts' (DTCs) are a new institution in North American criminal justice for dealing with drug offenders. There are currently two DTC pilot trials in implementation in Canada. Based on a `therapeutic jurisprudence' approach, DTCs claim to rehabilitate rather than punish drug-addicted offenders, and thus to reduce drug use, recidivism and social cost. Given the current enthusiasm about DTCs, this article provides a critique of DTCs' rationale, practices and implications from evaluative, social and legal perspectives. It focuses on the Canadian experience where possible. The analysis examines: the limitations of the evidence and methods behind the claims of DTCs' effectiveness; the ontological and practical challenges of the proposed `bridging' of punishment and treatment; the legal and penological implications of `therapeutic jurisprudence' practices; as well as potential explanations for DTCs' rise in popularity despite the limited evidence for their positive impact. The article concludes with reflections on the implications of DTCs for the government of drug use as deviance in contemporary social contexts, as well as for political efforts towards less punitive drug control. DTCs may reinforce the hegemony of punitive drug use control—partly by coopting treatment strategies—and thus fundamentally protect the legitimacy of prohibition politics.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.029
GPT teacher head0.312
Teacher spread0.283 · 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

Citations59
Published2003
Admission routes3
Has abstractyes

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