Bibliographic record
Abstract
This chapter explores the broader context and history of race-related issues in the UK, considering why racial disparities persist in diverse societies like the US, Australia, Canada, and the UK, before narrowing the focus to race and ethnicity in the sphere of crime and criminal justice. The concepts of ‘race’ and ‘ethnicity’ have long played major roles in both classroom and broader societal discussions about crime, punishment, and justice, but they have arguably never been more present and visible than today. The chapter looks at the problems with the statistics available on race, ethnicity, and crime, noting the ways in which they may not tell the whole story, before considering the statistics themselves as the chapter discusses the relationships between ethnicity and victimisation and offending. It then moves on to how ethnic minorities experience the various elements of the criminal justice system and the disadvantages they often face, before outlining the attempts that have been made to address these disparities at a state level. Finally, the chapter discusses critical race theory, a key theory in modern criminological examinations of race and its relationship to crime and justice, which grew out of the US but has much broader value and relevance as a framework of analysis.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".