The Social Network of Victims of Domestic Violence: A Network-Based Intervention Model to Improve Relational Autonomy.

This study aims to understand when and how domestic violence victims' relational autonomy changes and to propose an intervention model stemming from the findings. Using qualitative and social network analysis, we study the actions of network members, as well as changing features of victims' networks. Results show that victims base their decisions on their expectations toward others, and on a desire to preserve their autonomy. Their relational autonomy tends to increase when they leave abusive partners and stay in shelters, but maintaining relational diversity proves challenging once they exit shelters. A network-based model of intervention that aims to improve the victims' relational autonomy is proposed.

Assessing market competition and vendors' size and scope on AlphaBay.

BACKGROUND: Since 2011, drug market participants have traded illegal drugs through cryptomarkets, a user-friendly infrastructure in which drug market participants can conduct business transactions. This study assesses market competition and the size and scope of drug vendors' activities on one of the largest cryptomarkets, AlphaBay, in order to better understand the challenges that drug vendors face when selling on this venue. METHODS: Relying on data collected from AlphaBay, we calculate the degree of competition within the drug market using the Herfindhal-Hirshmann Index (HHI). We then follow a micro analytical approach and assess the size and scope of vendors' accounts. This is done by evaluating each vendor's market share over time using a group-based trajectory model (GBTM). Results from the GBTM are then used to assess vendors' exposure, diversity and experience based on their selling position in the market. RESULTS: The HHI scores demonstrate that cryptomarkets offer a highly competitive environment that fits in a top-heavy market structure. However, the distribution of vendors' market share trajectories shows that only a small portion of vendors (referred to as high-level vendors) succeed in generating regular sales, whereas the majority of vendors are relegated to being mere market spectators with almost zero sales. This inequality is exacerbated by the aggressive advertising of high-level vendors who post many listings. Overall, product diversity and experience is limited for all market participants regardless of their level of success. We interpret these results through Reuter's work on traditional illegal markets, e-commerce studies and the growing field of cryptomarket research. CONCLUSION: We conclude that, while offering a new venue for illegal drug transactions, in many ways, the economics of cryptomarkets for drug dealing are consistent with Reuter's classic assessment of illegal markets and the consequences of product illegality that underlie it. Cryptomarkets conflicting features, a relatively open setting with relatively high barriers to entry and sales, shape the competitive, yet top-heavy market that emerges from our analysis. This creates a challenging environment for cryptomarket drug dealers.

Modeling Verdict Outcomes Using Social Network Measures: The Watergate and Caviar Network Cases.

Modelling criminal trial verdict outcomes using social network measures is an emerging research area in quantitative criminology. Few studies have yet analyzed which of these measures are the most important for verdict modelling or which data classification techniques perform best for this application. To compare the performance of different techniques in classifying members of a criminal network, this article applies three different machine learning classifiers-Logistic Regression, Naïve Bayes and Random Forest-with a range of social network measures and the necessary databases to model the verdicts in two real-world cases: the U.S. Watergate Conspiracy of the 1970's and the now-defunct Canada-based international drug trafficking ring known as the Caviar Network. In both cases it was found that the Random Forest classifier did better than either Logistic Regression or Naïve Bayes, and its superior performance was statistically significant. This being so, Random Forest was used not only for classification but also to assess the importance of the measures. For the Watergate case, the most important one proved to be betweenness centrality while for the Caviar Network, it was the effective size of the network. These results are significant because they show that an approach combining machine learning with social network analysis not only can generate accurate classification models but also helps quantify the importance social network variables in modelling verdict outcomes. We conclude our analysis with a discussion and some suggestions for future work in verdict modelling using social network measures.

Rethinking Compassion Fatigue Through the Lens of Professional Identity: The Case of Child-Protection Workers.

Compassion fatigue is currently the dominant model in work-related stress studies that explain the consequences of caring for others on child-protection workers. Based on a deterministic approach, this model excludes the role of cognition a priori and a posteriori in the understanding of the impact of caregiving or providing social support. By integrating the notion of professional identity, this article adds a subjective perspective to the compassion fatigue model allowing for the consideration of positive outcomes and takes into account the influence of stress caused by accountability. Mainly, it is argued that meanings derived from identity and given to situations may protect or accelerate the development of compassion fatigue or compassion satisfaction. To arrive at this proposition, the notions of compassion fatigue and identity theory are first reviewed. These concepts are then articulated around four work-related stressors specific to child-protection work. In light of this exercise, it is argued that professional identity serves as a subjective interpretative framework that guides the understanding of work-related situations. Therefore, compassion fatigue is not only a simple reaction to external stimuli. It is influenced by meanings given to the situation. Furthermore, professional identity modulates the impact of compassion fatigue on psychological well-being. Practice, policy, and research implications in light of these findings are also discussed.

Cannabis cultivation in Quebec: between space-time hotspots and coldspots.

BACKGROUND: Cannabis cultivation has become increasingly localized, whether soil-based or hydroponic growing methods are used. Characteristics of a given location, such as its climate and the equipment it requires may influence general accessibility or attract different types of offenders based on potential profits. The location of crops, especially hydroponic crops, suggests a certain proximity to the consumer market via semi-urban and urban environments, while making it possible to avoid detection. This article examines the cannabis market through its cultivation. METHODS: The stability of temporal and spatial clusters of cannabis cultivation, hotspots, and coldspots between 2001 and 2009 in the province of Quebec, Canada, are addressed. Studying the geography of crime is not a new endeavor, but coldspots are rarely documented in drug market research. Using arrests and general population data, as well as Kulldorff's scan statistics, results show that the temporal distribution of cannabis cultivation is highly seasonal for soil-based methods. RESULTS: Hydroponic production shows adaptation to its soil-based counterpart. Stable patterns are found for both spatial distributions. Hotspots for soil-based cultivation are found near several urban centers and the Ontario border. For hydroponic cannabis cultivation, a new hotspot suggests the emergence of an American demand for Quebec-grown cannabis between 2007 and 2009. Curiously, the region surrounding Montreal, the largest urban center in Quebec, is a recurrent and stable coldspot for both methods of cultivation. CONCLUSION: For all periods, spatial clusters are stronger for soil-based methods than in the hydroponic context. Temporal differences and spatial similarities between soil-based cultivation and hydroponic cultivation are discussed. The role of the metropolis is also addressed.