Bibliographic record
Abstract
This book traces the history, theory, methodology and future direction of cultural criminology. Drawing on issues of representation, meaning and politics, this book walks you through the key areas that make up this fascinating approach to the study of crime. The second edition has been fully revised to take account of recent developments in this fast developing field, thereby keeping you up-to-date with the issues facing cultural criminologists today. It includes: • A new chapter on war, terrorism and the state • New sections on cultural criminology and the politics of gender, and green cultural criminology • Two new and expanded chapters on research methodology within the field of cultural criminology • Further Reading suggestions and a list of related films and documentaries at the end of each chapter, enabling you to take your studies beyond the classroom • New and updated vignettes, examples, and visual illustrations throughout • A new concluding chapter Building on the success of the first edition, Cultural Criminology: An Invitation offers a vibrant and cutting-edge introduction to this growing field. It will encourage you to adopt a critical and contemporary approach to your studies in criminology. First edition: 2009 Distinguished Book Award from the American Society of Criminology's Division of International Criminology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.020 |
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".