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Record W2073782001 · doi:10.1080/13552600.2010.544415

Ever-increasing circles: A descriptive study of Hampshire and Thames Valley Circles of Support and Accountability 2002–09

2011· article· en· W2073782001 on OpenAlexaboutno aff
Andrew Bates, Ron Macrae, Dominic Williams, Carrie Webb

Bibliographic record

VenueJournal of Sexual Aggression · 2011
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsnot available
Fundersnot available
KeywordsAccountabilityPsychologyPrisonConvictionIntervention (counseling)Suicide preventionPoison controlCriminologyPolitical scienceLawMedicinePsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

Abstract This paper gives a history of Hampshire and Thames Valley (HTV) Circles, an organisation which recruits, trains and supports volunteer members of the public who are formed into small groups meeting weekly to provide support and monitoring of post-conviction sex offenders (Core Members) in the community. It describes the origins of Circles of Support and Accountability in Canada and gives an account of its implementation in the UK and a summary of the findings of the previous study of the first 16 HTV Circles Core Members in 2006 and some discussion about the challenges inherent in evaluating this kind of community-based and volunteer-led intervention. It describes demographic data on 60 Core Members followed-up for an average period of 36.2 months, including offence and sentence category, treatment history and statistically assessed risk of reconviction. It provides evidence of progress by these Core Members across a range of dynamic risk factors, as well as information on sexual reconviction, recall to prison and dropout from Circles. Three case studies provide details of Circles practice in community risk management of sex offenders. The paper discusses proposed areas of further research into Circles work, as well as the development of new techniques for measuring and managing dynamic risk factors displayed by Core Members in the community.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.071
GPT teacher head0.322
Teacher spread0.252 · 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.

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

Citations39
Published2011
Admission routes1
Has abstractyes

Explore more

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