Privatisation in criminal Justice: key issues and debates
Bibliographic record
Abstract
In recent years, the criminal justice sector has made various strategic partnerships with the private sector, exemplified by initiatives within the police, the prison system, offender services and legal defence. This has seen unprecedented growth in the past quarter of a century, and a veritable explosion under the tenure of the Conservative Liberal Coalition government in the United Kingdom. This book explores the social, cultural, and political context of privatization in the criminal justice sector. Key areas of domestic and global concern are highlighted and illustrated with detailed case studies of important developments. It connects the study of criminology and criminal justice to the wider study of public policy, government institutions, and political decision making and provides a theoretical and practical framework for evaluating collaborative public and private sector response to social problems at the beginning of the twenty-first century.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".