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The purpose of this paper is to examine temperament differences, notably effortful control and negative emotionality, and correlates that distinguish between homicide, violent sexual and other violent juvenile offenders. Exploring heterogeneity among violent offenders is relevant to intervention strategies and policy implications.

Demographic measures, temperament constructs and individual risk factor indicators were assessed across 30,303 violent juvenile offenders (including 397 homicide offenders) in Florida to assess their ability to distinguish among violent juvenile offender subgroups.

Analyses demonstrated temperament constructs distinguish among classifications of violent juvenile offenders with effortful control differentiating homicide and violent sexual offenders from other violent offenders, and negative emotionality distinguishing violent sexual from other violent offenders, with youth having greater negative emotionality and less effortful control being non-sexual violent offenders. Homicide offenders were more likely to be older, male and had histories of gang membership and weapon/firearm offending than other violent offenders, and evidenced greater negative emotionality than violent sexual offenders.

The differences across violent youthful offender subtypes suggest heterogeneity among violent offenders with distinct correlates more predictive of some subtypes than others. Additionally, the temperament constructs of effortful control and negative emotionality are useful in distinguishing violent offender subtypes, which points toward differing intervention/treatment strategies.

This study assesses the relationship between job satisfaction, perceived organizational support and workplace factors on officer turnover intention within an urban, municipal police organization.

Using data from an online survey of New York City Police Officers (n = 1,823), both bivariate analysis and logistic regression models were utilized to assess the salience of police officer job satisfaction, perceived organizational support and perception of six workplace domains, including financial compensation, environmental factors, professional fulfillment, work/quality of life balance, treatment from management and occupational risk, on predicting turnover intention.

The cross-sectional study finds that job satisfaction, financial factors (salary, benefits and retirement benefits) and fulfillment predict lower levels of turnover intention (i.e. higher levels of organizational commitment). Work–life balance and environmental factors (cleanliness of work environment and condition of equipment) predict higher levels of turnover intention. Both perceptions of organizational support and occupational risk, while significant in the bivariate models, were not significantly associated after accounting for other factors. There is no evidence that officer perception of public support or the risk of being injured/killed at work were related to officer turnover intention.

The current study is limited by its focus on only one police department and its use of cross-sectional data, which may limit the generalizability of the results to agencies that differ in size and type and do not allow for assessment of causality.

Officer turnover intention may be reduced by increasing financial compensation, improving the work environment and promoting a healthy work–life balance.

The study contributes to a growing body of research on police officer voluntary turnover by evaluating established predictors along with workplace factors in an urban police department: the setting where officer turnover intention is hypothesized to be the greatest.

Purpose: To critically explore the implications of the August 2020, decision by Carleton University’s Institute for Criminology and Criminal Justice (ICCJ) to end to its intern program with the Ottawa police, the RCMP, Correctional Services Canada and Ottawa-Carleton Detention Centre starting in Fall 2021.

Findings: In contrast to the negative reaction of Kevin Haggerty to this decision, the authors offer a strong but qualified endorsement of the ICCJ’s move to put an end to its internship with coercive institutions. The ICCJ strategically mobilized discourses of anti-Blackness and inclusion in response to the murder of George Floyd and the individual and communitarian traumas of Black, First Nations and Metis and students colour in its program. The ICCJ did not, however, substantively engage with the ways that criminology, sociology and the university are complicit through the legitimation practices and processes of ideology, professionalization and research in the ‘violence work’ of the state. The critique, ethics and logical conclusion of abolitionism are obfuscated.

Methodology/Approach: The authors explicitly draw on the Black Radical Tradition, Neo-Marxism and radical neo-Weberianism to sketch research possibilities that resist the university as a space of violence work, both in criminology and in the professionalization of policing.

Originality/Value: The debate between the ICCJ and Kevin Haggerty is an important opportunity to critically analyze the limits of critical criminology and lacunae of a debate about abolitionism, anti-criminology and university-state nexus as a site for the production of ideological and hardware violence work. Grounded in the Black Radical Tradition, neo-Marxism and radical neo-Weberianism, the authors sketch a framework for a research agenda toward the abolition of criminology.

The purpose of this paper is to develop, apply and validate a mesh-free graph theory–based approach for rapid thermal modeling of the directed energy deposition (DED) additive manufacturing (AM) process.

In this study, the authors develop a novel mesh-free graph theory–based approach to predict the thermal history of the DED process. Subsequently, the authors validated the graph theory predicted temperature trends using experimental temperature data for DED of titanium alloy parts (Ti-6Al-4V). Temperature trends were tracked by embedding thermocouples in the substrate. The DED process was simulated using the graph theory approach, and the thermal history predictions were validated based on the data from the thermocouples.

The temperature trends predicted by the graph theory approach have mean absolute percentage error of approximately 11% and root mean square error of 23°C when compared to the experimental data. Moreover, the graph theory simulation was obtained within 4 min using desktop computing resources, which is less than the build time of 25 min. By comparison, a finite element–based model required 136 min to converge to similar level of error.

This study uses data from fixed thermocouples when printing thin-wall DED parts. In the future, the authors will incorporate infrared thermal camera data from large parts.

The DED process is particularly valuable for near-net shape manufacturing, repair and remanufacturing applications. However, DED parts are often afflicted with flaws, such as cracking and distortion. In DED, flaw formation is largely governed by the intensity and spatial distribution of heat in the part during the process, often referred to as the thermal history. Accordingly, fast and accurate thermal models to predict the thermal history are necessary to understand and preclude flaw formation.

This paper presents a new mesh-free computational thermal modeling approach based on graph theory (network science) and applies it to DED. The approach eschews the tedious and computationally demanding meshing aspect of finite element modeling and allows rapid simulation of the thermal history in additive manufacturing. Although the graph theory has been applied to thermal modeling of laser powder bed fusion (LPBF), there are distinct phenomenological differences between DED and LPBF that necessitate substantial modifications to the graph theory approach.

A novel approach to modeling exchange rates is presented based on a set of models distinguished by the drivers of the rate and regime duration. The models are combined into a “meta model” using model averaging and non-nested hypothesis-testing techniques. The meta model accommodates periods of stability and slowly evolving or abruptly changing regimes involving multiple drivers. Estimated meta models for five exchange rates provide a compelling characterization of their determination over the last 40 years or so, identifying “phases” during which the influences from policy and financial market responses to news succumb to equilibrating macroeconomic pressures and vice versa.

Intercollegiate athletics is a tremendous part of today’s society and encompasses numerous American lives. Its wide spectrum attracts various people from gender, race, ethnicity, cultures, religion, and sexuality. Black male student-athletes, a target of higher education institutions, are affected by sociological, institutional, and athletic factors. This population is highly sought after by college coaches due to their athletic abilities and ability skills in their specific sport in order to elevate their respective sports team, athletics’ department, and university into the national limelight. Current institutional and intercollegiate athletics’ trends that are incorporated to recruit Black male student-athletes are explored. Specific recruiting techniques utilized by college coaches to persuade this population are examined. The sociological issues in current intercollegiate athletics are analyzed with their direct effect on the college selection choice of Black male student-athletes.

This chapter is a retrospective commentary on the efficacy of teaching and learning in a higher education space that embraces the incredible diversity of delivery modes available in the post-Covid-19 era of “Smart Cities.” The current reality of widespread and leading-edge experimentation with online learning necessitates that existing brick-and-mortar institutions reimagine their places as providers of higher education in this new age of digital disruptions that will resonate with all stakeholders a future of endless possibilities. The authors, with four decades between them of practice and field research at leading universities and colleges in Australia, Canada, New Zealand, Singapore, and UAE, advocate an approach to higher education that is personalized for learning effectiveness, industrial operations, and institutional evolution; that is, a higher education that is democratized. They warn that the wasted opportunities of meaningful digital transformation pre-Covid-19 have led to an urgency of transformation at the present time. While randomized control trials continue to be the “elephant in the room”; scholars, leaders, technocrats, and regulators must drive the quest for the growth and relevance of a diversified and learner-driven higher education in the years ahead. The platform of a “smart city” may just be the catalyst for such a radical innovation.

Digital transformation has gained particular interest among academics and policymakers in recent years. However, the empirical quantification of digital transformation stages and their impact on innovation in small and medium-sized enterprises (SMEs) remains understudied. Therefore, this study aims to investigate the impact of digital transformation stages on a differentiated measurement of innovation performance in SMEs.

The authors propose a simplified one-dimensional digital maturity path to estimate the stages of digital transformation in SMEs. The authors validate their approach with a cluster analysis and perform an ordered logistic regression to estimate the impact of digital transformation stages on SMEs' innovation performance.

The authors' results show that digital transformation in general has a positive impact on SMEs' innovation performance. More precisely, the authors find that the early stage of digital transformation has a detrimental effect on innovation performance, while significant and positive effects can be expected from the experimental stage onward. Furthermore, the advanced stage of digital transformation significantly increases the probability of producing radical innovations.

This study contributes to the ongoing discussion about the relationship between digital transformation and innovation in SMEs by presenting an approach to quantify digital transformation stages in SMEs. Additionally, this study provides new insights into the specific dynamics of the relationship between different stages of digital transformation and their impact on a differentiated measurement of innovation performance, including technological, non-technological and radical innovation.

The PMI Risk Framework (PRF) is introduced as a guide to classifying and identifying risks which can be the source of post-merger integration (PMI) failure — commonly referred to as “culture clash.” To provide managers with actionably insight, PRF dissects PMI risk into specific relationship-oriented phenomena, critical to outcomes and which should be addressed during PMI. This framework is a conceptual and theory-grounded integration of numerous perspectives, such as organizational psychology, group dynamics, social networks, transformational change, and nonlinear dynamics. These concepts are unified and can be acted upon by integration managers. Literary resources for further exploration into the underlying aspects of the framework are provided. The PRF places emphasis on critical facets of PMI, particularly those which are relational in nature, pose an exceptionally high degree of risk, and are recurrent sources of PMI failure. The chapter delves into relationship-oriented points of failure that managers face when overseeing PMI by introducing a relationship-based, PMI risk framework. Managers are often not fully cognizant of these risks, thus fail to manage them judiciously. These risks do not naturally abide by common scholarly classifications and cross disciplinary boundaries; they do not go unrecognized by scholars, but until the introduction of PRF the risks have not been assimilated into a unifying framework. This chapter presents a model of PMI risk by differentiating and specifying numerous types of underlying human-relationship-oriented risks, rather than considering PMI cultural conflict as a monolithic construct.

In this chapter, the authors explore the concept of actorial identity through analysing the construction of legal persons as actors, centred on the argument that there is an ontological separation between living men and women and their legal representations. The authors propose an analytical frame based in part on the games studies literature, wherein actorial identities known as ‘Avatars’ are created by performative declarations that articulate Avatars with Players (living persons). The Avatars act within a bounded ‘Matrix’ while being controlled by Players who are outside the Matrix. In applying the frame to the legal Matrix, the authors distinguish between living persons, natural persons and artificial persons, and introduce the concepts of first-order and second-order Avatars. The authors then employ the analytical frame to model the use of legal Avatars by Apple Inc. and illustrate how cryptocurrency technology enables the creation of Avatars that can transact outside legal systems. The frame also helps explain how autonomous systems could acquire actorial identity and then functionally participate in the legal Matrix.