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Record W2555680540

Prolific and Priority Offenders in British Columbia, Canada: A Preliminary Analysis of Recidivism1

2012· article· en· W2555680540 on OpenAlexaboutno aff
Stefanie N. Rezansoff, Akm Moniruzzaman, Julian M. Somers

Bibliographic record

VenueInternational Journal of Criminal Justice Sciences · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCriminologyApprehensionCriminal justiceConvictionPsychological interventionPsychologyMental healthTherapeutic jurisprudencePolitical sciencePublic relationsPsychiatryLaw
DOInot available

Abstract

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IntroductionThere is emerging consensus in correctional literature that a disproportionate amount of crime (particularly property crime) is committed by a minority of offenders (Marlow, 2007; Mawby & Worrall, 2004; Millie & Erol, 2006; Vennard & Pearce, 2004). There is also general agreement among scholars that custody alone is a relatively ineffective method of reducing offending (Moore et al., 2006), and that 'getting tough on crime' has been unsuccessful (Andrews & Bonta, 2010). These realizations have spurred a variety of programs in several countries that emphasize inter-agency collaboration as a means of improving offender outcomes and increasing community safety (Mawby & Worrall, 2004). Illustrative programs are predicated on the emerging concept of therapeutic jurisprudence (Public Safety Canada and Alberta Solicitor General, 2010) and growing evidence that successful criminal justice policy requires a focus on evidence-based offender treatment and rehabilitation practices (Andrews & Bonta, 2010).A central feature of this new approach is targeted and intensive offender surveillance, with a core component involving coordinated police and probation partnerships or polibation (see Nash, 1999). These partnerships are further supported by an integrated team of service providers that deliver interventions tailored to local priority and prolific offenders including substance use and mental health treatment, as well as referrals to employment, income assistance and housing programs. This assertive outreach is complemented by a second component of the strategy - prompt apprehension and conviction following re-offending or breaches of sentence conditions (Worrall & Mawby, 2004). Swiftresponse to re-offending is expedited by dedicated Crown Counsel who manage files generated as a result of the targeting and increased of offenders. To date, these strategies have predominantly applied toward chronic property crime offenders (Mawby & Worrall, 2004; Merrington, 2006; Vennard & Pearce, 2004).By design, these programs give rise to two quite different outcome measurements, each implying possible program success (i.e., a reduction in convictions and timely reconvictions). As a consequence, the clear selection of appropriate measures of effectiveness has important implications for research and evaluation. The majority of the available evaluations are based on initiatives in England and Wales (see Roberts, 2005). Variations of the scheme have also been evaluated in Australia, the United States and at the federal level in Canada (Pottruff, 2010). While the US has become the world leader in intensive programs (ISP) for adult offenders, it is important to note that there is no standardized ISP model (Moore et al., 2006). Few peer-reviewed outcome analyses of such programs exist.Whereas deterrence is the cornerstone of most US initiatives (which date back to the 1960s), UK projects tend to include a corresponding focus on offender treatment and rehabilitation (Moore et al., 2006). Worrall & Mawby (2004) describe three generations of intensive supervision (p.268). These began in the early 1970s with a now infamous project called IMPACT (Intensive Matched Probation and After-Care Treatment), which involved matching offenders to various types of probation interventions. Evaluations reported very unfavorable results, including higher rates of recidivism among program participants than non-participants (Worrall & Mawby, 2004). A number of similar ISPs followed in the 1980s and early 1990s. These also included a focus on surveillance and were primarily targeted at young adults between the ages of 17 and 25 (Moore et al., 2006). While these were also largely unsuccessful at reducing recidivism, evaluations did identify a number of secondary benefits associated with the projects (Worrall et al., 2003). Fast tracking of drug assessments and treatments were recognized as particularly valuable (Worrall, 2001). …

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)0.001
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 score0.475
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.032
GPT teacher head0.326
Teacher spread0.294 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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