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This article explores the possibility of measuring the impact of law enforcement on organized crime in a reliable and accountable manner, both in general terms and with a practical focus on the Canadian context. In considering measures to combat organized crime, a focus on process measurement has obscured the more substantial question of progress as regards the dependent variable itself: the bottom line of reducing the impact of organized criminal behaviour. While outcome measures are more challenging to identify than process measures, this fact alone does not minimize the need to demonstrate the connection between organized crime enforcement and its presumed outcomes to a greater degree of certainty. To date, this has not been realized to any significant degree, as revealed by a review of existing international approaches to measuring the impact of enforcement activity. The article argues that a multidisciplinary focus on community level indicators of crime, if initially less accessible than process measures of impact on organized crime groups, offers promise as a measurement of absolute and relative impact of state investment in enforcement.
Allan CastleEmail:
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In the current study, by drawing on the literature concerning peer deviance and criminological social learning theory, and on theory and research concerning cultural variability in uncertainty avoidance, we offer a rationale for predicting that peer deviance will be lower among Japanese than among Americans. The lower level of peer deviance among Japanese, in turn, might explain why crime and deviant behaviour are less common in Japan compared to the United States. Analyses of comparable survey data from college students in Japan and the United States (N = 1271) provide strong support for our hypothesis.  相似文献   
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Qualitative Comparative Analysis (QCA) is a well-established method for comparing national public policy similarities and differences. It is argued that Cluster Analysis can add additional benefits to such research when used concurrently with QCA. Cluster Analysis provides a better method for the initial exploration of multivariate data and examining how countries compare because it can work with the full range of available interval data while patterns are created and viewed. This provides the best first method for exploring patterns and likely groupings of countries. QCA then provides a more robust method for theorizing about the construction of such groupings and their relationship around similar variable scores. QCA makes such theorizing transparent. The research example used to illustrate the benefits of combining Cluster Analysis and QCA is an analysis of the evolving of macroeconomic policy for the countries sharing the Euro, comparing 2005 (precrisis) with 2010 (postcrisis).  相似文献   
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