What Kind of Justice Today ? Expectations Of 'Good Justice', Convergences And Divergences Between Managerial And Judicial Actors And How They Fit Within Management-Oriented Values

This research aims toward a better understanding of the organizational culture(s) of the judiciary in Switzerland by analysing what 'good justice' means nowadays in this country. It seeks to clarify whether, and to what extent, expectations of 'good justice' of judicial actors (judges without managerial experience) and of managerial actors (court managers) are similar and to describe possible managerial implications that may result from this. As judges are at the heart of the judicial organization and exert a strong influence on other groups of actors (Sullivan, Warren et al. 1994), the congruence of their expectations with those of court managers will be at the centre of the analysis. Additionally, referring to the conceptual worlds of Boltanski and Thévenaut (1991), we analyze how closely these expectations are to management-oriented values. We found that almost half of expectations are common to the two groups examined and the main quoted ones are compatible to new public management (NPM) concepts. On the other hand, those expectations shared exclusively by judges relate to the human side of justice, whereas those specific to court managers focus on the way justice functions.

A Bayesian network approach to the database serch problem in criminal proceedings

Background The 'database search problem', that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity. Methods As a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population. Results This paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches. Conclusions The proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions. The method's graphical environment, along with its computational and probabilistic architectures, represents a rich package that offers analysts and discussants with additional modes of interaction, concise representation, and coherent communication.