Bibliographic record
Abstract
In countries such as the United States, Britain, Canada, France, Australia, New Zealand and the Netherlands, the high proportion of Indigenous peoples and ethnic and racial minorities incarcerated or in the criminal justice system has raised questions and much debate about the link between criminal justice, race and class (Mauer and King, 2007). The figures from two of these countries, by way of illustration, are stark. For example, according to the Federal Bureau of Investigation’s (FBI) 2019 Uniform Crime Report, 26.6 per cent of all arrests and 36.4 per cent of arrests for violent crime in the United States were Black or African American, despite comprising only 14 per cent of the population (FBI, 2019; Tamir et al, 2021). ‘Of adults arrested for murder, 51.3 per cent were Black or African American [and] 45.7 per cent were White’ (FBI, 2019). In terms of juvenile violent crime, African Americans comprised nearly 49 per cent of all juveniles arrested in 2020 (Office of Juvenile Justice and Delinquency Prevention, 2020). Piquero and Brame (2008: 1–2) reveal that ‘the non-White arrest rate for robbery was 132.8 per 100,000, whereas the comparable rate for Whites was 23.0 per 100,000’ and also that ‘the non-White to White robbery ratio was 5.773, indicating that for every one White who was arrested for robbery, almost six non-Whites were arrested’ for the same crime. In fact, Piquero and Brame conclude that the arrest rates for non-Whites were higher than White arrest rates for all crimes. Of note, however, is the disparity between the rates of drug arrests and the rates of drug offending among minorities. Mitchell and Caudy (2015, cited in Drakulich and Rodriguez-Whitney, 2018: 19) find that ‘at ages 17, 22, and 27 African-Americans’ chances of drug arrest are approximately 13, 83, and 235% greater than whites, respectively’, despite the fact that ‘African-American and Hispanics reported statistically lower rates of drug offending [compared to Whites] on nearly every measure of drug offending’. Drakulich and Rodriguez-Whitney (2018) further note that African-American youth are twice as likely to report having had some contact with the police, highlighting the racialized imbalances between contact with the justice authorities and actual offending, and also the relatively early start to experiencing these racialized encounters.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".