The literature considers intimate partner violence (IPV) perpetrators to be a heterogeneous group. In Portugal, IPV perpetrators may be sentenced to either of two types of sentences: deprivation of freedom or alternatives to prison (in the community). In the present study, we performed a comparative analysis between 76 male individuals sentenced to prison and 76 male individuals sentenced to community measures for crimes against their current or former partners, using a set of psychological measures. The results indicate that although incarcerated and nonincarcerated IPV perpetrators demonstrate considerable differences, some similarities may also be observed. Incarcerated IPV perpetrators present the lowest socioeconomic status and education, perpetrate more severe IPV, and have more criminal convictions. A hierarchical regression analysis revealed that the use of weapons and objects increased the probability of IPV perpetrators belonging to the prison group above and beyond sociodemographic variables. Based on these results, implications for IPV perpetrators' treatment are discussed.

Male Perpetrators of Intimate Partner Violence: A Comparison Between Incarcerated and Community Offenders.

http://www.cell.com/article/S2405844023005510/pdf

A quantitative evaluation of judicial justice in civil cases with Chinese free trade zone enterprises as the plaintiff

This study identifies factors that contribute to sentencing outcomes for criminally sentenced individuals experiencing mental disorders, in two U.S. states with divergent sociopolitical ideologies. Recent case law (n = 130) from appellate courts in New York and Kansas (from 2020 to 2021) was analyzed using regression and machine learning to predict sentence severity for individuals experiencing mental disorders. Across both states, trauma-related and personality disorders led to the most severe sentences, while paraphilia, addiction, and mood disorders had the lowest probability of imprisonment. Sentencing outcomes in Kansas were significantly more severe as compared with New York. A classification analysis identified important patterns of sentencing determinants that predicted which mental disorders were more likely to lead to incarceration. Findings and implications are discussed in relation to punishment disparities as well as the potentials and pitfalls regarding the use of machine learning approaches in criminal justice research and policy.

Mental Illness as a Sentencing Determinant: A Comparative Case Law Analysis Based on a Machine Learning Approach

The current study attempts to explain how judge sentencing varies as a response to the legal and extralegal factors of drug offenders. The variables of interest in this research are the recidivism factors among drug offenders. The utilization of ordinary least squares (log-level regression) gives results that mostly coincide with prior studies. However, the empirical evidence found indicates that the roles, gender, religion, and birthplaces of offenders reveal a different fact. This paper acknowledges the limitations related to the data of ethnicity, judge characteristics, and demographic situation in each province. Hence, this model could be improved in future research.

/pdf/the-impact-of-legal-and-extra-legal-factors-on-severity-of-dhp6gsqr.pdf

The impact of legal and extra-legal factors on severity of judges sentencing regarding narcotics offenders

https://cyberleninka.org/article/n/1231935.pdf

Oancea Gabriel

Papers

Legal and Extralegal Factors Influencing Judge's Penal Decisions