Comparing risks & needs assessment policies and practices in Canada and Hong Kong
Bibliographic record
Abstract
Risk and needs assessments are actuarial based instruments that aim to evaluate an (1) offender’s risks including the risk of reoffending, (2) criminogenic needs so they can be targeted in treatment and (3) offender responsivity inclusive of the learning style, motivation, abilities and strengths of the offender (Andrews, Bonta and Wormith, 2011, 735). \n\nSince 2006, looking to Western nations as exemplars, the HK Security Bureau’s policy initiatives have introduced a Risk and Needs Assessment Protocol for all local young offenders, and local adult offenders with sentences of two years and above. But one has to question how the policy transfer applies here in Hong Kong. What can Hong Kong’s criminal justice policy makers and practitioners adapt from research conducted in Canada and the United States? Is there anything HK officials can learn from other jurisdictions, both in terms of experiences implementing risk needs tools and the wider socio-cultural context under which such implementation takes place? \n\nThis study has provided some preliminary answers to these questions through critical analysis and expert interviews. Subsequent analysis on the definition of risk and need under the HK CSD’s protocol outlined a further need for a definition of the responsivity principle. Concerns over the content of responsivity enhancement programs along with its effects on the voluntary participation of young offenders were also discussed in this analysis. \n\nSince the initial consultancy was commissioned by the CSD in 2002 to empirically develop and refine the protocol, a follow up study was much needed to suggest improvements. This study has served to fulfill this goal by suggesting improvements in addressing class, gender and racial disparity along with suggestions on operational excellence. Specifically, interviews with leading Canadian risk assessment experts including criminologists and practitioners highlighted four main challenges and three main lessons for HK CSD to examine (p. 57-58). \n\nInterviews with Hong Kong risk/needs assessment experts including criminologist and HK CSD practitioners help provide clarification on the risk/need assessment process and how rehabilitative programs operate. Additional analysis on the risk/need assessment instrument used in Hong Kong along with an examination of the questions used by assessors was subsequently conducted. The result challenges the CSD’s Risks and Needs Assessment and Management Protocol for Offenders as a “scientific and evidence based approach to prison management and offender rehabilitation” (CSD Booklet, 201, 3). This conclusion is based on the many social assumptions made on offenders found in the assessment tool and ambiguous design of questions used to evaluate criminogenic need.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".