Why Are Federal Meth Sentences Getting Longer?

While sentence lengths for most federal drug trafficking offenses have decreased in recent years, methamphetamine sentences are moving in the opposite direction, lengthening by 12% between FY2015 and FY2019. Using data from the U.S. Sentencing Commission and other sources, I consider several possible reasons for this increase. I conclude that four recent trends have jointly produced longer meth sentences: (1) drug volumes have increased, (2) the criminal history of the average offender has become more extensive, (3) weapon enhancements and charges have become more common, and (4) cases have grown increasingly likely to be sentenced as high-purity “ice” or by “actual” meth content, which carry much more punitive mandatory and guideline minimums than meth mixture. How much of the increase in sentence lengths has been attributable to shifting case characteristics (e.g., growing drug volumes, changing criminal histories, and increased weapons use) versus efforts to charge and pursue offenses that carry greater penalties? I use USSC data to conduct several simulations estimating how sentence lengths would have evolved if all meth cases were sentenced as the same meth type. I predict that the average meth trafficking sentence would have lengthened by 27–33% less, or 3.3–4.0 fewer months, if all cases were sentenced as the same meth type but all other case attributes remained unchanged. The remainder of the growth is attributable to case and offender characteristics. However, this prediction assumes that relief and leniency decisions would not change if statutory and guideline minimums were altered; to allow for this possibility, I run another set of simulations, taking these possible offsetting effects into account. My latter simulations predict that trafficking sentences might have increased 13–16% less than they did in reality, a smaller magnitude than my initial estimates. I briefly consider the underlying reasons for these trends. Some, but not all, of the changing offense characteristics may be linked to the recent shift to Mexican methamphetamine production. The timing of the shift in meth type charged suggests it may largely be the result of a change in Justice Department charging policy enacted in 2017; this shift cannot be attributed to any change in drug purity.

Bayesian Crime Investigations: Integrating Actuarial And Expert Models

In response to concerns that some of the most methodologically rigorous predictive studies of criminal offender characteristics may yet be less generalizable and applicable than advertised or assumed, this research first tests how well seven regression analysis models (represented by 28 equations) predict characteristics across three conditions: familiar cases (used to create the regressions), less familiar cases (native to the sample used to create the regressions) and foreign cases (from a similar but novel sample). Here a linear trend shows overfitting of the models to their own sample: a drop-off in prediction accuracy relative to simple mean-based prediction as cases become more foreign (ηp 2 = .646). In response to hopes that subjective input from expert police investigators could be integrated into the models to correct for this overfitting bias, this research also tests an algorithm combining expert ratings with the regression equations. Here moderate and significant improvement in novel-case prediction is observed overall (p = .036, r = .44) and equations for all twelve expert participants are shown to improve prediction to varying degrees. These results suggest that current best methods would perform poorly in the field, but can be improved by expert insight.

Bayesian Crime Investigations: Integrating Actuarial And Expert Models

In response to concerns that some of the most methodologically rigorous predictive studies of criminal offender characteristics may yet be less generalizable and applicable than advertised or assumed, this research first tests how well seven regression analysis models (represented by 28 equations) predict characteristics across three conditions: familiar cases (used to create the regressions), less familiar cases (native to the sample used to create the regressions) and foreign cases (from a similar but novel sample). Here a linear trend shows overfitting of the models to their own sample: a drop-off in prediction accuracy relative to simple mean-based prediction as cases become more foreign (ηp 2 = .646). In response to hopes that subjective input from expert police investigators could be integrated into the models to correct for this overfitting bias, this research also tests an algorithm combining expert ratings with the regression equations. Here moderate and significant improvement in novel-case prediction is observed overall (p = .036, r = .44) and equations for all twelve expert participants are shown to improve prediction to varying degrees. These results suggest that current best methods would perform poorly in the field, but can be improved by expert insight.

Sex Offender Characteristics: Analysis Based on a Sample of Hungarian Prisoners

The present study introduces the differences in sex offender groups primarily from the perspective of prison, focusing on the attributes that determine placement, safety, and the conditionality of therapeutic programs. The study is primarily based on the risk analysis and registration database of the Hungarian Prison Service. The result of the study is that perpetrators of sexual abuse against children, and perpetrators of sexual assault against adults showed significant differences in each reintegration needs and in-prison risks, including their background and childhood, in-prison and out-of-prison vulnerability, integration issues, suicide risk, and other areas. Those who abuse children show a unique picture different from all other groups of offenders, while violent sex offenders targeting adults show similarities in most areas to violent offenders in the control group.

Integrating the Literature on Lethal Violence: A Comparison of Mass Murder, Homicide, and Homicide-Suicide

Although mass murder is traditionally examined as a separate construct from homicide generally, few studies have explored their similarities and differences. This study compares the incident, victim, and offender characteristics of: (1) mass murderers and homicide offenders; and (2) mass murder-suicide offenders and homicide-suicide perpetrators. Mass murderers are more likely to be male; commit suicide; kill young, white, and female victims; use firearms; co-offend; operate in public places; and kill as part of drug trafficking and/or gang warfare. The analysis demonstrates that mass murderers are distinct from both homicide and homicide-suicide perpetrators, and represent a unique type of violent offender.

Innocence Lost

The sexual abuse of students in grades K-12 by educators/school personnel is an understudied phenomenon in society. The present chapter explores and describes the extant empirical research on students sexually victimized by educators with an emphasis on offense prevalence, victim characteristics and behaviors, offender characteristics and behaviors, contextual incident characteristics, initiatives to address the problem, and shortcomings that impede awareness and knowledge. Shortcomings include a lack of national-level data collection, perception and delegitimization, transferring alleged offenders to other school systems, and reporting practices. This chapter provides readers with contemporary information on the scope and scale of educator sexual abuse through the description of these invisible victims, their offenders, and incident characteristics of the offense.

Stalking Recidivism: A Comparison of Operational Definitions

The criminal offense of stalking is somewhat different to other offenses due to the repetitive, innocuous, and often multifaceted nature of the crime. Given that stalking constitutes a number of different behaviors, such as violence and threats, research on stalking recidivism becomes difficult as recidivism can be defined in a number of ways. This study utilized a dataset of Western Australia Police Force incident reports, comprising a sample of 404 stalking offenders. Survival curves and a binomial logistic regression were used to determine time to recidivism and predictors of recidivism, using four different definitions of recidivism. Predictor variables included age of the offender, prior history of criminal charges, and offender ethnicity. The four definitions ranged from narrow (a new stalking charge) to broad (any new criminal charge). The results of the study show that stalkers reoffend quickly, however our understanding of how fast and which offender characteristics predict recidivism, is dependent on how we define recidivism. This highlights the importance of considering how stalking recidivism is defined in future works and may explain current differences in stalking recidivism findings.