Racial and ethnic differences in the consequences of school suspension for arrest

A growing body of literature has demonstrated that when schools suspend students, the suspension acts not as a deterrent but as an amplifier of future punishment. Labeling theory has emerged as the predominant explanation for this phenomenon, suggesting that the symbolic label conferred along with a suspension shapes how other people perceive and respond to labeled students. Few studies, however, have attended to racial/ethnic differences in this process even though critical race theory suggests the consequences of suspension likely differ across racial/ethnic groups due to prevailing racial/ethnic stereotypes. This study uses six waves of data from the National Longitudinal Study of Youth 1997 (N = 8,634) to examine how the relationship between suspension and subsequent arrest differs for White, Black, and Hispanic students. Using a series of within-person analyses that control for time-stable personal characteristics, this study finds that suspension amplifies Black and Hispanic students’ risk of arrest relative to that of White students. White students’ risk of arrest was not amplified by suspension and, in some models, was diminished. This study's findings underscore the importance of understanding the labeling process as different by race/ethnicity and indicate that suspension is particularly harmful for Black and Hispanic relative to White students.     

Prevalence and blood concentrations of benzodiazepines and opioids in opioid-positive death investigations in Ontario,Canada, from 2017 to 2021

The aim of this study was to investigate the incidence of benzodiazepines in opioid-positive death investigations, including trends in frequency and combination of drugs, as well as demographic data and blood concentrations, where available. Additionally, naloxone concentrations in polysubstance compared to opioid-only cases were analyzed. This was a retrospective study that consisted of all post-mortem toxicology cases in Ontario, Canada, from January 01, 2017, to December 31, 2021, with an opioid finding in any analyzed autopsy specimen. There were 11,033 death investigations identified. The overall rate of benzodiazepine co-involvement was 54.5%. Males accounted for the majority of cases (71%), and the most affected age group was 30- to 39-year-olds. The most frequently detected opioid was fentanyl and the most frequently detected benzodiazepine was etizolam, which was also the most frequently observed opioid/benzodiazepine combination. Findings related to differences in concentrations of opioids when naloxone was also present were mostly non-significant, except for methadone. The rate of benzodiazepine detection with opioids grew faster than opioid detections overall, potentially due to the increasingly toxic drug supply. Detection of novel psychoactive drugs fluctuated more unpredictably than opioids and benzodiazepines associated with clinical use. These findings can help inform policy decisions by public health agencies in exploring harm reduction efforts, for example, education and drug-checking services.     

Identification of the veterinary sedative medetomidine in combination with opioids and xylazine in Maryland

Public health, public safety, and forensic science personnel continue to face the emergence of new compounds into the drug market. While focus is often put on the detection of new analogs of known illicit drugs, monitoring the changes in cutting agents and other compounds can be equally as important. Over the last year, near real-time monitoring of the drug supply in Maryland has been completed through a public health–public safety partnership whereby residue from suspected drug packaging or used paraphernalia is collected and analyzed. Through this project, we have recently detected the presence of the veterinary sedative medetomidine in a small number of samples. The presence of medetomidine has been identified in both public health and law enforcement samples and in the presence of fentanyl and xylazine—another veterinary sedative that has been widely observed over the last year. While the rate at which medetomidine has been detected remains low, it is concerning and worthy of continued monitoring.     

Effects of language mismatch in automatic forensic voice comparison using deep learning embeddings

In forensic voice comparison, deep learning has become widely popular recently. It is mainly used to learn speaker representations, called embeddings or embedding vectors. Speaker embeddings are often trained using corpora mostly containing widely spoken languages. Thus, language dependency is an important factor in automatic forensic voice comparison, especially when the target language is linguistically very different from that the model is trained on. In the case of a low-resource language, developing a corpus for forensic purposes containing enough speakers to train deep learning models is costly. This study aims to investigate whether a model pre-trained on multilingual (mostly English) corpus can be used on a target low-resource language (here, Hungarian), not represented by the model. Often multiple samples are not available from the offender (unknown speaker). Samples are therefore compared pairwise with and without speaker enrollment for suspect (known) speakers. Two corpora are used that were developed especially for forensic purposes and a third that is meant for traditional speaker verification. Speaker embedding vectors are extracted by the x-vector and ECAPA-TDNN techniques. Speaker verification was evaluated in the likelihood-ratio framework. A comparison is made between the language combinations (modeling, LR calibration, and evaluation). The results were evaluated by Cllr_min and EER metrics. It was found that the model pre-trained on a different language but on a corpus with a significant number of speakers can be used on samples with language mismatch. Sample duration and speaking style also seem to affect the performance.     

Lead isotope measurement of primer gunshot residues and likelihood ratio predictions for forensic cartridge discrimination and individualization in China

Gunshot residues (GSR), cartridge projectiles, and casings are frequently encountered evidence in gun-related forensic investigations. However, in circumstances where the investigation of striation marks is impossible, such as unrecovered or deformed projectiles and cartridge casings, GSR deposited on the hands or clothes of the shooter and victim-related items can provide information to establish a link between the suspect, the firearms used, and the victim. Since the formula of primers used by all cartridge manufacturers in China is identical, links based on the conventional morphological and compositional analysis of GSR are difficult to establish. However, the abundance of lead isotopes in primer components of lead styphnate varies significantly, and a fundamental understanding of these differences may facilitate the validation of primer (p)GSR evidence in forensic investigations. Here, 44 pGSR samples were characterized by Pb isotope ratios of ²⁰⁶Pb/²⁰⁴Pb, ²⁰⁷Pb/²⁰⁴Pb, and ²⁰⁸Pb/²⁰⁴Pb using laser ablation multicollector inductively coupled plasma mass spectrometry. There was no obvious mass fractionation of the lead isotope ratios of the primers from individual cartridges analyzed before and after the shooting process, thereby establishing a basis for the comparison of pGSR and unfired cartridges. Evaluation of the results using univariate likelihood ratio (LR) computations revealed low rates of misleading evidence (<0.53%) The results demonstrated that lead isotope ratio analysis of pGSR and LR predictions can provide a practicable method for forensic cartridge discrimination and individualization.     

ForensicNet: Modern convolutional neural network-based image forgery detection network

The advancements in Image editing techniques produce realistic-looking artificial images with ease. These images can easily circumvent the forensic systems making the authentication process more tedious and difficult. To overcome this problem, we introduce a modern convolutional neural network (CNN) named ForensicNet, inspired by the recent developments in computer vision. The three major contributions of our CNNs are inverted bottleneck, separate downsampling layers, and using depth-wise convolutions for mixing information in the spatial dimension. The inverted bottlenecks help improve accuracy and reduce network parameters/FLOPs. The separate downsampling layers help converge the network. The normalization layers also help stabilize training whenever the spatial resolution is changed. The depth-wise convolution is a grouped convolution where the number of groups and channels are the same. The experiments show that ForensicNet outperforms the state-of-the-art methods by a large margin.