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Record W3038506560 · doi:10.1080/10509674.2020.1784350

The role of the defense attorney in relation to biological interventions as rehabilitative strategies

2020· article· en· W3038506560 on OpenAlexaffabout
Colleen M. Berryessa, Jennifer A. Chandler

Bibliographic record

VenueJournal of Offender Rehabilitation · 2020
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPsychological interventionCriminal justicePsychologyPerceptionRehabilitationCriminologyGrounded theoryQualitative researchPsychiatrySociology

Abstract

fetched live from OpenAlex

We present a qualitative analysis, employing semi-structured interviews and grounded theory, on the perceptions of defense attorneys regarding their roles and duties in contexts involving quasi-coercive offers of biological interventions, such as medication-assisted treatment therapies for opiate dependence or chemical castration, as rehabilitative strategies in sentencing. Data are from interviews with a sample of Canadian defense attorneys. We focus our analysis on defense attorneys’ views of their roles and duties as an attorney in contexts involving the decision to consent to biological interventions in the hope of securing favorable sentences, as well as their perceptions and concerns regarding their clients’ interests in these contexts. Our analysis appears to indicate that the classical model of the role and duties of defense lawyers can accommodate the increased use of biological therapies for criminal rehabilitation, and we ultimately discuss broader implications of biological treatments as rehabilitative strategies for the criminal justice system.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.487
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.053
GPT teacher head0.354
Teacher spread0.302 · 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 designObservational
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

Citations2
Published2020
Admission routes2
Has abstractyes

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