Race, Ethnicity, Crime, and Justice: An International Dilemma
Bibliographic record
Abstract
Race, Ethnicity, Crime, and Justice: An International Dilemma, Second Edition, takes a unique comparative approach to the exploration of race- and ethnicity-related justice issues in five countries around the world. Using the colonial model as a theoretical lens, Owusu-Bempah and Gabbidon analyse data from Great Britain, the United States, Canada, Australia, and South Africa. These international case studies help students contextualize race and justice issues within and across nations. Concise historical framing illuminates today’s racial dynamics in these diverse justice systems, and accessible theory grounds the comparison of crime and justice data from the early 21st century with current statistics. A new concluding chapter revisits the question of where these nations fit in the global context of state and non-state actors and of ethnic and racial justice issues. This new edition is suitable for use as a core or supplemental text for advanced undergraduates and early graduate courses on race and crime, minorities and criminal justice, diversity in criminal justice, and comparative justice systems. It is also appropriate for use in sociology and ethnic studies courses that focus on race and crime.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".