Bibliographic record
Abstract
Why is punishment not more effective? Why do we have such high re-offending rates? How can we deal with crime and criminals in a more cost-effective way? Over the last decade in particular, the United Kingdom, in common with other jurisdictions such as Canada, the United States (US) and Australia, has sought to develop more effective ways of responding to criminal behaviour through court reforms designed to address specific manifestations of crime. Strongly influenced by developments in US court specialisation, problem-solving and specialist courts - including domestic violence courts, drugs courts, community courts and mental health courts - have proliferated in Britain over the last few years. These courts operate at the intersection of criminal law and social policy and appear to challenge much of the traditional model of court practice. In addition, policy makers and practitioners have made significant attempts to try to embed problem-solving approaches into the criminal justice system more widely. Through examination of original data gathered from detailed interviews with judges, magistrates and other key criminal justice professionals in England and Wales, as well as analysis of legislative and policy interventions, this book discusses the impact of the creation and development of court specialisation and problem-solving justice. This book will be essential reading for students and academics in the fields of criminology, criminal justice, criminal law, socio-legal studies and sociology, as well as for criminal justice practitioners and policy-makers.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".