A human rights-based practice framework for sexual offenders

Abstract

Human rights create a protective zone around people and allow them the opportunity to further their own valued personal projects without interference from others. In our view, the emphasis on community rights and protection may, paradoxically, reduce the effectiveness of sex offender rehabilitation by ignoring or failing to ensure that offenders’ core human interests are met. In this paper we consider how rights-based values and ideas can be integrated into therapeutic work with sex offenders in a way that safeguards the interests of offenders and the community. To this end we develop a rights-based normative framework (the Offender Practice Framework: OPF) that is orientated around the three strands of justice and accountability, offender needs and risk, and the utilization of empirically supported interventions and strength-based approaches. We examine the utility of this framework for the different phases of sex offender practice.     

On The Application of Fuzzy Clustering for Crime Hot Spot Detection

   One of the fundamental challenges in crime mapping and analysis is pattern recognition. Efforts and methods to detect crime hot-spots, or geographic areas of elevated criminal activity, are wide ranging. For aggregate data, such as total crime events in a census tract(s), measures of spatial autocorrelation have proven useful. For disaggregate data (i.e. individual crime events), kernel density smoothing and non-hierarchical cluster analysis (e.g. k-means), are widely used. Non-hierarchical techniques are particularly effective in delineating geographic space into areas of higher or lower crime concentrations, because each observation is assigned to one and only one cluster. The resulting set of partitions provides clear-cut spatial boundaries that can be used for hot-spot evaluation and interpretation. However, the strength of non-hierarchical methods can also be viewed as a weakness. Although the hard-clustering of observations into a set of discrete clusters is helpful, there are many cases where ambiguity exists in the data. In such cases, a more generalized approach for hot-spot detection would be helpful. The purpose of this paper is to explore the use of a generalized partitioning method known as fuzzy clustering for hot-spot detection. Functional and visual comparisons of fuzzy clustering and two hard-clustering approaches (medoid and k-means), across a range of cluster values are analyzed. The empirical results suggest that a fuzzy clustering approach is better equipped to handle intermediate cases and spatial outliers.

Small-scale irrigation in sub-Saharan Africa: Sparse lessons,big problems,any solutions?

This paper identifies from studies made of specific schemes the major social and administrative problems confronting the improvement and extension of small-scale irrigation in Africa. The first section reviews the studies and notes the conclusions to be drawn from each separate one. Underlying these separate conclusions six general problems are identified and in the second section of the article each of these six is elaborated and discussed: the relation between the direct producer'S benefit and wider social benefits: problems of control, commitment to hierarchy, the working of production units, and how to learn from farmers in respect of water use; and finally the general problem of how to plan for the further change that follows after irrigation is introduced.