Journal of Criminal Justice最新文献

Purpose

Despite the precipitous rise of cryptocurrency in recent years, little is known regarding the utility and legitimacy of such fiat currencies. This research assesses whether or not a “cryptoculture” exists, and if so, its influence on fraud and/or cryptocurrency loss.

Methods

Logistic Regression was used to examine the relationship between cryptoculture and cryptocurrency loss or fraud in a sample of 919 survey respondents who previously purchased cryptocurrency.

Results

Results support the existence of a cryptoculture. Respondents who reported higher levels of confidence in cryptocurrency were significantly more likely to also report fraud or loss. This was despite also admitting to feeling more vulnerable when purchasing cryptocurrency. These seemingly conflicting results may actually point to a false sense of confidence by cryptopurchasers driven by cryptoculture, which encourages purchasing decisions to outweigh initial skepticism or distrust.

Conclusions

Underlying mechanisms of cryptocurrency purchasing decisions (like cryptoculture) should continue to be examined in greater depth. Over one-third of cryptocurrency purchasers reported that at least some of their cryptocurrency portfolio is missing, yet many remain confident in their ability to navigate the market. This study provides a nuanced analysis of the factors influencing how victims of fraud may be influenced to participate in risky purchases.

Research has recognized Adult Drug Treatment Courts (DTCs) as benefitting participants, with a wide body of research demonstrating lower levels of recidivism and drug use. A budding community-level body of research, however, has returned mixed results, some studies showing increases in arrests and crime relative to DTC initiation. Since DTCs cover over three-fourths of the US population, results showing such unintended consequences must be validated and rectified if held. This study estimated effects for DTCs for community-level crime effects from 1990 to 2018 using a stacked event study identification strategy. Most results indicated no significant effects. However, for population groups between 10,000 and 50,000, DTCs were associated with reductions in some crime categories. Violent index offenses offered the most robust results, and there was a small increase in non-index crimes in communities with populations between 50,000 and 100,000.

Background

Research on the association between depression and crime is limited regarding different types of crime and severity of crime.

Aim

Using Swedish national register data, we explored the association between depression and various crime outcomes and the severity of crime.

Methods

We identified 72,057 individuals with an outpatient depression diagnosis between 2001 and 2013. Individuals with depression were age-, sex-, and socioeconomic status (SES)-matched to undiagnosed population controls (n = 1,080,855). Logistic regression models estimated odds of any, violent, property, drug-related and non-violent crime, as well as the severity of crime.

Results

In fully adjusted models, individuals with depression had significantly higher odds of any conviction (adjusted OR [adjOR] 1.66 [95 % CI 1.60–1.71]). This pattern persisted across all crime types (violent, property, drug-related, non-violent), and for crime severity, including increased odds for longer sentences and multiple convictions.

Conclusions

Individuals with depression had an increased risk for convictions across all crime types and more severe crimes. Our results emphasize the importance of early identification of risk factors and targeted treatments, and the necessity for clarification of the underlying mechanisms of our findings.

Purpose

Parental history of criminal offending is a major risk factor for later criminal behavior in children. Extensive research has also shown low resting heart rate (RHR), a moderately heritable biological variable, to be prospectively predictive of criminal behavior. Despite its status as a replicable risk factor, limited research exists on RHR's role in the intergenerational transmission of crime. Specifically, it remains unclear whether parent-child resemblance for biological characteristics such as RHR might play a role in intergenerational crime transmission.

Method

The current study was undertaken to clarify the role of RHR in the intergenerational transmission of crime, and test for moderating effects of socioeconomic status (SES) on its role, in a large Swedish population-based sample of fathers and their sons combined (N ∼ 266,000).

Results

Beyond replicating prior work documenting paternal crime history and RHR as predictors of later offspring crime, we show that father–son resemblance for RHR accounts in part for father-to-son crime transmission, and that familial SES does not moderate this transmission.

Conclusions

Our findings have important implications for understanding the role of biological and environmental influences in the intergenerational transmission of crime.

Anti-police sentiment has emerged as a growing phenomenon in recent years, characterised by intense negative attitudes towards police. Despite increasing scholarly interest, the lack of a clear definition of anti-police sentiment hinders conceptualisation, measurement, and comparison of findings across jurisdictions. This study aims to provide conceptual clarity by employing sentiment analysis to systematically and objectively explore the emotions underlying anti-police sentiment. Analysis of the negative discourse across 1140 comments on Reddit about police, found that anti-police sentiment is associated with the emotions anger, fear, and frozenness, and potentially may trigger fight, flight, freeze responses in citizens encountering the police. Further, analysis of frequent terms highlights key social issues driving anti-police sentiment, such as police responses to domestic violence and police misconduct. The study contributes to the understanding of anti-police sentiment by identifying the underlying emotions and their potential behavioural outcomes, providing a foundation for future research and policy interventions.

Purpose

For the last five decades, gangs have been a criminal justice priority, commanding the attention of police, courts, and corrections. Legislative bodies and local, state, and federal agencies have adapted or engineered policies and practices to combat the influence of gangs. While these efforts have been subject to inquiry by researchers and media, they have largely been uninformed by public opinion.

Methods

We surveyed 1000 adults in the United States to understand public support for five common yet controversial responses to gangs spearheaded by the criminal justice system.

Results

There was a widespread consensus of support for police gang databases, civil gang injunctions, Racketeering Influenced and Corrupt Organizations (RICO), and sentencing enhancements, but not solitary confinement. Respondents were not swayed by the experimental presentation of evidence of racial disparities in gang databases or the psychological impacts of solitary confinement; support for civil gang injunctions fell when learning of potential constitutional violations but not violence reduction; support for RICO fell when learning of the organizational structure of street gangs and the potential for guilt-by-association; and sentencing enhancements were no longer supported upon learning the financial cost.

Conclusions

Public opinion maintains a significant role in criminal justice policy and practice on gangs that criminologists should subject to research and evaluation. This research underscores the importance of evidence-based policy formulation and the need for ongoing dialogue between the public, researchers, and policymakers to address challenges as complex and inexorable as gangs and gang violence in communities.

Purpose

Sentencing practices in cases involving defendants with mental disorders are often opaque, as data on case facts and sentencing decisions are not easily accessible.

Methods

This paper reports findings from a national U.S. sample of appellate court cases across 46 states (n = 710) that involved mental health evidence. We collected detailed data on judge and defendant characteristics, type and severity of mental disorders, state sociopolitical ideologies, and legal factors such as offense and plea type and criminal history. We used a mixed quantitative approach, including machine learning, to examine how these intricate factors influence sentencing outcomes.

Results

A combination of linear regressions and supervised learning techniques reveals important differences in sentencing outcomes based on the type of mental disorder as well as the majority political ideology of states. We additionally show that, as compared to arguing no mental health evidence, having a mental disorder generally did not yield significant differences in sentencing.

Conclusions

Both a potential lack of scientific comprehension and the influence of sociopolitical ideology may help explain why certain mental disorders are aggravating in punishment contexts. We also discuss the advantages and limitations of supervised learning and classification trees for studying judicial decisions.

Juvenile justice systems routinely utilize risk assessment instruments (RAIs) to guide the provision of services and custody decisions to optimize youth treatment and system resources. While research has evaluated the validity of RAIs in predicting recidivism risk for justice-involved youth, it has yet to assess the feasibility of using RAIs in a prevention context. The early age and lack of formal delinquency history among this population pose considerable challenges to RAI effectiveness. Drawing data from the Florida Department of Juvenile Justice, analyses explored a sample of 11,472 Prevention Assessment Tool (PAT) assessments administered to youth aged 10 to 16 who had no prior system contact. Off-the-shelf scoring and optimized scoring developed through multivariate and machine learning techniques were employed to validate a prevention risk assessment instrument that predicts the risk of juvenile justice contact until age 18. The results indicate exemplary RAI performance among all optimized statistical techniques across various validation metrics. While performance was consistent across demographic subgroups, there were indications that predictive validity varied across age groups. These findings support the use of RAIs in early intervention and prevention services.

Purpose

This study investigates whether the Police Reform Bill significantly reduced overall incidents of police misconduct, including those involving the use of force, in Massachusetts.

Methods

The current study employed a monthly interrupted time series (ITS) design using data on overall incidents of police misconduct and incidents involving the use of force, collected from the 438 law enforcement agencies in Massachusetts. The Police Reform Bill was introduced in December 2020. The monthly data covers the period from January 1989 through December, 2023 (N = 420), with January 1989 through December 2021 serving as the period before the implementation of the Police Reform Bill (N = 384) and January 2022 through December, 2023 as the period following the implementation of the Police Reform Bill (N = 36).

Results

The results of the monthly interrupted time series analyses showed that, compared to before the implementation of the Police Reform Bill, the trend in the overall number of police misconduct incidents and the number of misconduct incidents involving the use of force significantly declined after the implementation of the Police Reform Bill.

Conclusions

The findings suggest that the Police Reform Bill is effective in reducing police misconduct and misconduct incidents involving the use of force.

Purpose

Identifying high-risk adolescents prone to homicidality, linked to serious criminal activities and homicide, offers vital avenues for homicide prevention.

Methods

This study analyzed data from 1596, 1596, and 1526 students at baseline, one-year, and two-year follow-ups, respectively, drawn from the Chinese Early Adolescent Cohort study. Based on Bronfenbrenner's ecological systems theory, predictors of adolescent homicidality were categorized into individual, family, and school and peer levels. Five machine learning methods were utilized to construct prediction models for homicidality risk and to pinpoint predictive factors.

Results

Logistic regression models using only significant features effectively predicted adolescent homicidality and new onsets in the short term, as well as homicidal trajectories throughout early adolescence. Key factors identified included suicidal ideation, emotional abuse, life satisfaction, physical violence, and verbal violence, with suicidal ideation and emotional abuse emerging as the most critical predictors.

Conclusions

This study successfully developed risk-predictive models for adolescent homicidality using machine learning, emphasizing suicidal ideation and emotional abuse as primary predictors. These findings highlight the importance of targeted interventions focused on these key variables for the early prevention of adolescent homicide.