When is a Sentence a Miscarriage of Justice?
Bibliographic record
Abstract
Abstract The first part of this chapter examines how disproportionate sentencing discounts have contributed to guilty plea wrongful convictions that form a significant number of remedied wrongful convictions in the United Kingdom, the United States, and Canada. Attempts to regulate such discounts have been unable to stop such miscarriages of justice in part because of charge bargaining. The second part of the chapter examines how Criminal Cases Review Commissions (CCRCs) in England and Wales, Scotland, and New Zealand have exercised their jurisdiction to refer sentences back to appeal courts. The exclusion of sentencing from the jurisdiction of a commission in North Carolina and in Canada’s proposed commission is related to Julian V Roberts’ diagnosis of penal populism and its particular strength in North America. CCRCs refer sentences based on investigations that discover new facts and factual and legal errors that influenced sentencing. They could also serve as an alternative means by which to challenge parole decisions and introduce new facts to sentencing, including those related to offender disadvantage. Many sentencing referrals in England and Wales, however, relate to technical and legal errors in calculating sentences that could be avoided by simpler laws and better education of trial judges about sentencing. The chapter confirms Julian V Roberts’ important claim that sentencing is central to the criminal process by demonstrating how sentences can lead to wrongful convictions and how sentences based on legal and factual errors can themselves be characterized as miscarriages of justice.
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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 | 0.005 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".