Sentencing Members of Minority Groups: Problems and Prospects for Improvement in Four Countries
Bibliographic record
Abstract
Members of racial, ethnic, and Indigenous minorities have long accounted for disproportionate percentages of prison admissions in Western nations and of prison populations. The minorities affected vary between countries. Discriminatory or differential treatment by criminal justice officials from policing through to parole is part of the problem. Much media and professional attention focuses on sentencing, where the decision-making is most public. An emerging body of research identifies sentencing as a cause—or, at the very least, an amplifier—of minority overincarceration. Solutions aiming to reduce it have been implemented, with varying but modest degrees of success, in the United States, England and Wales, Canada, and Aotearoa New Zealand. Progress toward reducing minority overincarceration has been slow. Most US sentencing commissions have failed to determine the extent to which their guidelines contribute to the problem. The Sentencing Council of England and Wales has taken the limited step of warning judges about racial disparities, without suggesting remedial steps to be taken. Courts in Canada and Aotearoa New Zealand have taken more activist approaches, mitigating sentences when offenders adduce evidence of discrimination or abuse by criminal justice officials.
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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".