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Record W2086161401 · doi:10.1080/10683160500337592

Risk and need assessment in British probation: the contribution of LSI-R

2006· article· en· W2086161401 on OpenAlexaboutno aff
Peter Raynor

Bibliographic record

VenuePsychology Crime and Law · 2006
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsRisk assessmentPsychologyService (business)CriminologyCommunity serviceActuarial sciencePublic relationsBusinessPolitical scienceManagementEconomicsMarketing

Abstract

fetched live from OpenAlex

From 1996 until about 2000 the Canadian Level of Service Inventory – Revised (LSI-R) was in use in a number of probation services in England and Wales, and it is still in use in the Jersey Probation and After-Care Service. This article reviews what has been learned about risk and need assessment in British probation through the use of LSI-R, drawing on data collected for a Home Office study and for evaluative research in Jersey. Particular areas of interest are accuracy, differences between male and female offenders, the comparative effectiveness of probation and community service, the apparent counterproductive impact of probation on low-risk offenders, and the efficacy of risk-related change measurement. The conclusion points out the wide-ranging advantages of risk/need assessment for probation services, and discusses why services in England and Wales have been slow to benefit from this.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score0.882

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.004
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.424
Teacher spread0.398 · 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 source (direct Gemma or distilled Codex), 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

Citations103
Published2006
Admission routes1
Has abstractyes

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