Risk and the management of crime
Bibliographic record
Abstract
There is now a well-established international consensus amongst criminal justice policy makers and practitioners that various forms of risk management can be used as predictive tools. Internationally governments identify crime as a major problem which needs to be managed through various forms of individualised actuarially based risk assessment. Risk management constitutes a significant paradigm change within criminal justice practice throughout Europe, the USA, Canada, Australia, and New Zealand. In this chapter risk-based policies and practices will be placed within the context of a wider societal shift towards the ‘risk society’ in neo-liberal and social democratic states over the last twenty years. Although the contours of criminal justice policy have been shaped by a political preoccupation with risk, there are important differences in policy approaches to crime, personal, and collective security. It will be argued that ‘problem, policy, and political streams’ drive ever changing and frequently contradictory master risk narratives. Risk discourses are modified and recreated by politicians in an attempt to read and respond to public ‘mood’. However, concentration on individualised risk assessment has diverted attention from the risks to public safety created by structural inequalities. Moreover, an over-emphasis on risk appears in many instances to be accompanied by a more punitive approach, particularly to young offenders.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".