Bibliographic record
Abstract
Canada’s criminal justice landscape has been shaped by contrary trends in recent years. As the crime rate declines, policy-makers continue to push for tough-on-crime legislation, and university criminology programs continue to expand. Given these trends, what does the future hold for criminology and criminal justice? This book presents the work of a new generation of critical criminologists who explore the geographical, institutional, and political context of the discipline in Canada. Breaking away from mainstream criminology and popular law-and-order discourses, the authors present a spectrum of theoretical approaches to criminal justice – from governmentality to feminist criminology, from critical realism to anarchism – and they propose novel approaches to topics such as genocide, white-collar crime, and the effect of prison sentences on families. By posing crucial questions and attempting to define what criminology should be, this book will shape debates about crime, policing, and punishment for years to come.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.026 | 0.012 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".