“The devil destroyed us”: Satanism and gender violence in South Africa

In October 2011, a young South African woman named Kirsty Theologo was set on fire and left for dead by a group of her high school contemporaries in Linmeyer, Johannesburg. The killing was defined as a “Satanist murder,” leading to media, judicial, and religious interventions aimed at countering the apparent Satanic threat. This article examines press material surrounding Kirsty Theologo’s death and the subsequent arrest and trial of her killers. It argues that the ongoing moral panic around Satanism in contemporary South Africa has obscured the often gendered nature of so-called satanic violence, showing the way in which the murder was instead placed within a familiar framework of Satanic panic in an act of collective displacement that elided the structural and historical contexts of acts of extreme violence perpetrated by young South African men on the bodies of young South African women.     

WinBUGS - A Bayesian modelling framework: Concepts,structure, and extensibility

WinBUGS is a fully extensible modular framework for constructing and analysing Bayesian full probability models. Models may be specified either textually via the BUGS language or pictorially using a graphical interface called DoodleBUGS. WinBUGS processes the model specification and constructs an object-oriented representation of the model. The software offers a user-interface, based on dialogue boxes and menu commands, through which the model may then be analysed using Markov chain Monte Carlo techniques. In this paper we discuss how and why various modern computing concepts, such as object-orientation and run-time linking, feature in the software's design. We also discuss how the framework may be extended. It is possible to write specific applications that form an apparently seamless interface with WinBUGS for users with specialized requirements. It is also possible to interface with WinBUGS at a lower level by incorporating new object types that may be used by WinBUGS without knowledge of the modules in which they are implemented. Neither of these types of extension require access to, or even recompilation of, the WinBUGS source-code.     

New managerialism in the academy: Gender bias and precarity

In the era of global competition, academic institutions are increasingly being managed as efficient organizations where early career academics are the most vulnerable group in the academic hierarchy. We use gender budgeting to deconstruct the financial and managerial processes and procedures in a selected academic institution in Iceland. Drawing on multiple data collection methods, we argue that new managerialism enhances the precarious position of early career academics, especially women and those in the more feminized fields. Furthermore, we show that the system's bias in favour of so‐called hard science generates gendered consequences for early career academics. We demonstrate this structural gender bias in each of the first three stages of an academic career: PhD, postdoc and other temporary positions, and assistant professorship. By highlighting the gendered consequences of new managerialism, we want to direct attention to the need to include a gender perspective in the budgeting and all the decision‐making processes in academic institutions.     

Parents' perspectives on young suicide

Strategies for the prevention of adolescent suicide are frequently designed to identify those young people who represent a high risk in order that services and support can be effectively targeted. This study explored the experiences of parents who had lost a child through suicide. The findings suggest that the range of behaviours perceived by parents was too broad and diverse to allow for a checklist approach to the identification of risk. The parents' responses did produce some valuable reflections on their experiences of professional support as well as some key messages on parenting which could be disseminated to all parents of adolescents.     

Hierarchical related regression for combining aggregate and individual data in studies of socio-economic disease risk factors

Summary.  To obtain information about the contribution of individual and area level factors to population health, it is desirable to use both data collected on areas, such as censuses, and on individuals, e.g. survey and cohort data. Recently developed models allow us to carry out simultaneous regressions on related data at the individual and aggregate levels. These can reduce 'ecological bias' that is caused by confounding, model misspecification or lack of information and increase power compared with analysing the data sets singly. We use these methods in an application investigating individual and area level sociodemographic predictors of the risk of hospital admissions for heart and circulatory disease in London. We discuss the practical issues that are encountered in this kind of data synthesis and demonstrate that this modelling framework is sufficiently flexible to incorporate a wide range of sources of data and to answer substantive questions. Our analysis shows that the variations that are observed are mainly attributable to individual level factors rather than the contextual effect of deprivation.