Ever-increasing circles: A descriptive study of Hampshire and Thames Valley Circles of Support and Accountability 2002–09
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".