Criminal justice and inequality: what can be done to reduce inequality?
Bibliographic record
Abstract
There are significant differences in outcomes among different ethnic groups who have come into contact with the CJS. Men from minority ethnic backgrounds tend to come into contact with the CJS at a younger age, form a larger proportion of those serving custodial sentences and, in the case of Black men, spend more of their original sentence in prison compared with men from other ethnic groups. The Lammy Review (2017) recommended that criminal justice organisations should be able to explain variations in outcomes and experiences across different ethnicities, or to reform CJS practices to achieve more equitable outcomes. At present, it is not possible to fully explain the variations in experiences in minority groups, particularly when they are released from prison. \n \nThis report provides an overview of the key issues pertaining to the experience of people from minority communities that need to be considered when supporting them as part of the process of leaving prison and reintegrating back into communities. Recommendations are included at each stage based on evidence emerging from the literature, and these are summarised again at the end of the report. Due to the previously noted lack of evidence within the UK context, we also draw on evidence from overseas, particularly the US. We acknowledge that there are different challenges and barriers in these contexts, but where areas of good practice are identified elsewhere, these should be considered to explore what lessons can be learned and applied to assist us in better supporting the desistance journeys of people within the UK.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".