Criminal justice and political cultures : national and international dimensions of crime control
Bibliographic record
Abstract
1. Criminal Justice and Political Cultures by Tim Newburn and Richard Sparks 2. Durkheim, Tarde and Beyond: The Global Travel of Crime Policies by Susanne Karstedt 3. Globalising Risk? Distinguishing Styles of 'Neoliberal' Criminal Justice in Australia and the USA by Pat O'Malley 4. Policing, Securitisation and Democratisation in Europe by lan Loader 5. The Cultural Embeddedness of Social Control: Reflections on a Comparison of Italian and North American Cultures Concerning Punishment by Dario Melossi 6. Controlling Measures: The Repackaging of Common-sense Opposition to Women's Imprisonment in England and Canada by Pat Carlen 7. The Convergence of US and UK Crime Control Policy: Exploring Substance and Process by Trevor Jones and Tim Newburn 8. Youth Justice: Globalisation and Multi-modal Governance by John Muncie 9. Importing Criminological Ideas in a New Democracy: Recent South African Experiences by Dirk van Zyl Smit and Elrena van der Spuy 10. Policy Transfer in Local Crime Control: Beyond Nave Emulation by Kevin Stenson and Adam Edwards 11. Containment, Quality of Life and Crime Reduction: Policy Transfers in the Policing of a Heroin Market by David Dixon and Lisa Maher
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".