Crime, Justice and Social Democracy: International Perspectives
Bibliographic record
Abstract
Crime, Justice and Social Democracy is a provocative and thoughtful collection of timely reflections on the state of social democracy and its inextricable links to crime and justice. Authored by some of the world's leading thinkers from the UK, US, Canada and Australia, with a preface from Professor David Garland of New York University, this volume provides a powerful social democratic critique of neoliberal regimes of governance and crime control on an international scale. Social democratic values raise broad questions about government, ethics, and the exercise of power in criminal justice institutions; each chapter here engages with how this might occur and with what consequences. The contributions to this volume, while critical and hard hitting, also boldly envision a more socially just criminal justice politic. This collection is essential reading for activists, scholars, legislators, politicians and policy makers who are concerned with promoting, imagining and understanding socially sustaining societies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".