Suchergebnisse

Abstract
Deferred prosecution agreements (DPAs) are a legal tool for the nontrial resolution of cases of corruption. Each DPA is accompanied by a Statement of Facts that provides detailed and publicly available textual records of the given cases, including summarized evidence of who was involved, what they committed, and with whom. These statements can be translated into networks amenable to social network analysis allowing an analysis of the structure and dynamics of each case. In this study, we show how to extract information about which actors were involved in a given case, the relations and interactions among these actors (e.g., communication or payments), and their relevant individual attributes (gender, affiliation, and sector) from five Statements of Fact. We code the extracted information manually with two independent coders and subsequently, we assess the inter-coder reliability. For assessing the coding reliability of nodes and attributes, we use a matching coefficient, whereas for assessing the coding reliability of ties, we construct a network from the coding of each coder and subsequently calculate the graph correlations of the two resulting networks. The coding of nodes and ties in the five extracted networks turns out to be highly reliable with only slightly lower coding reliability in the case of the largest network. The coding of attributes is highly reliable as well, although it is prone to missing data on actors' gender. We conclude by discussing the flexibility of our data collection framework and its extension by including network dynamics and nonhuman actors (such as companies) in the network representation.

Responses to current environmental challenges, such as the energy transition, require collaboration among diverse actors interacting in complex and conflicting policy settings. This study examines the drivers of inter-organizational collaboration within the conflictual context of Czech coal phase-out by investigating hypotheses on belief homophily, political influence, and expert information. It uses a sequential mixed-methods research design combining exponential random graph modeling, which controls for network self-organization processes, and directed qualitative content analysis, which validates and extends the findings from the previous stage. The results show that organizations perceived as influential and organizations providing expertise are more likely to be involved in inter-organizational collaboration. Belief homophily does not predict collaboration but is relevant for disincentivizing collaboration among actors with low-compatible beliefs, thus contributing to conflict reproduction. The study concludes that future collaborative arrangements need to avoid such design flaws as those of the recently established Coal Committee, which reinforced existing power asymmetries and conflicts.

AbstractThe crime gender gap is the difference between the levels of participation of men and women in crime, with men responsible for more crime than women. Recent evidence suggests that the crime gender gap is closing, both in crime in general and in organized crime. However, organized crime differs from other forms of criminal activity in that it entails an organizational structure of cooperation among offenders. Assessing whether the gender gap in organized crime is narrowing is not only about the overall levels of involvement of women, but about their roles and positions within the organized criminal structure, because the involvement of women does not mean that they are in influential positions, or that they have power or access to resources important for the commission of organized crime. This paper uses a social network approach to systematically compare the structural positions of men and women in an organized criminal network. We use a dataset collected by Canadian Law Enforcement consisting of 1390 individuals known or suspected to be involved in organized crime, 185 of whom are women. Our analysis provides evidence for an ongoing gender gap in organized crime, with women occupying structural positions that are generally associated with a lack of power. Overall, women are less present in the network, tend to collaborate with other women rather than with men, and are more often in the disadvantageous position of being connected by male intermediaries. Implications for theory and law enforcement practice are discussed.

The Czech Republic (or Czechia) is facing the second wave of COVID-19 epidemic, with the rate of growth in the number of confirmed cases (among) the highest in Europe. Learning from the spring first wave, when many countries implemented interventions that effectively stopped national economics (i.e., a form of lockdown), political representations are now unwilling to do that again, at least until really necessary. Therefore, it is necessary to look back and assess efficiency of each of the first wave restrictions, so that interventions can now be more finely tuned. We develop an age-structured model of COVID-19 epidemic, distinguish several types of contact, and divide the population into 206 counties. We calibrate the model by sociological and population movement data and use it to analyze the first wave of COVID-19 epidemic in Czechia, through assessing effects of applied restrictions as well as exploring functionality of alternative intervention schemes that were discussed later. To harness various sources of uncertainty in our input data, we apply the Approximate Bayesian Computation framework. We found that (1) personal protective measures as face masks and increased hygiene are more effective than reducing contacts, (2) delaying the lockdown by four days led to twice more confirmed cases, (3) implementing personal protection and effective testing as early as possible is a priority, and (4) tracing and quarantine or just local lockdowns can effectively compensate for any global lockdown if the numbers of confirmed cases not exceedingly high.

Crime research has grown substantially over the past decade, with a rise in evidence-informed approaches to criminal justice, statistics-driven decision-making and predictive analytics. The fuel that has driven this growth is data – and one of its most pressing challenges - is the lack of research on the use and interpretation of data sources. This accessible, engaging book closes that gap for researchers, practitioners and students. International researchers and crime analysts discuss the strengths, perils and opportunities of the data sources and tools now available and their best use in informing sound public policy and criminal justice practice