Gender and Criminal Sentencing: A Comparative Analysis of Global Judicial Biases and Reform Efforts
Bibliographic record
Abstract
This paper delves into the intersection of gender and criminal law, with a particular focus on how gender influences sentencing practices across various jurisdictions. Through a comparative analysis of countries including the United States, Canada, the United Kingdom, Australia, India, and South Africa, it uncovers significant disparities in sentencing outcomes between male and female offenders. These disparities are not merely incidental but are deeply rooted in cultural norms, societal expectations, and judicial biases, which vary significantly across different legal contexts. The paper also addresses the challenges associated with implementing gender-neutral sentencing guidelines, emphasizing the need for a nuanced approach that considers the complex social and psychological factors influencing female criminal behavior. Furthermore, it explores the role of judicial training and systemic reforms in mitigating gender biases, highlighting successful examples such as Canada’s Gladue Reports and the Corston Report in the UK. By examining these issues, the paper contributes to a deeper understanding of the pervasive gender biases in the criminal justice system and suggests practical pathways toward more equitable sentencing practices that can better align with the principles of justice and equality.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".