People with disability and new disaster communications: access and the social media mash-up

This article explores how a lack of access to increasingly complex and overlapping digital communications platforms in times of disaster for people with disabilities has the potential to make already life-threatening situations considerably more dangerous. As we are increasingly coming to rely on a social media mash-up of digital platforms to assist in communications during disaster situations, the issue of accessibility for people with disabilities is as dire as if it was high ground during a tsunami or transport during a typhoon. The contemporary social media environment is characterised by a complex and overlapping network of complementary platforms, populated by user-generated content, where people communicate and exchange ideas. In this environment, YouTube videos are posted to Facebook and embedded in blogs, and Twitter is used to link to these other sites and is itself embedded in other platforms. These networks are increasingly supplementing and supplanting more traditional communication platforms, such as the television and radio, particularly in times of disaster. The concern of this paper is that the elements from which this mash-up of communications channels is made are not always accessible to people with disabilities. This evolving network of social media-based communication exposes the limits of existing Internet-based universal design.     

Punishment and pleasure: women, food and the imprisoned body1

Food assumes enormous importance in prison: for many prisoners it conditions their life in custody and, in many respects, is symbolic of the prison experience. This article explores the complex relationship between gender, food and imprisonment through an analysis of data obtained from in‐depth interviews and group discussions conducted in three women's prisons in England. The findings indicate that, in prison, where control is taken away as the prisoner and her body become the objects of external forces, food is experienced not only as part of the disciplinary machinery, but also as a powerful source of pleasure, resistance and rebellion. The implications of such findings for health promotion in the prison context are discussed. Here, the pleasures and consolations of food may well constitute a redefinition of what it is to be healthy in this context, one that challenges the dominant meaning constructed in current health promotional discourse.     

A Bayesian method for classification and discrimination

We discuss Bayesian analyses of traditional normal-mixture models for classification and discrimination. The development involves application of an iterative resampling approach to Monte Carlo inference, commonly called Gibbs sampling, and demonstrates routine application. We stress the benefits of exact analyses over traditional classification and discrimination techniques, including the ease with which such analyses may be performed in a quite general setting, with possibly several normal-mixture components having different covariance matrices, the computation of exact posterior classification probabilities for observed data and for future cases to be classified, and posterior distributions for these probabilities that allow for assessment of second-level uncertainties in classification.     

Police Reforms in Child Abuse Investigation: Their Success and Limitations in the Struggle to Uphold Children's Rights

SUMMARY. The past decade has shown increased awareness, concern and understanding about the extent of child abuse, particularly sexual abuse, and the caring agencies response to listen to and to support the young victims. This article looks at the response of the police service over that decade to meet such demands and to work jointly and responsively with social services to further the welfare and protection of children. It also examines the constraints and limitations of the police and social services when they attempt to uphold the rights of child victims and to gain redress through the criminal justice system     

HEAVY-TAILED-DISTRIBUTED THRESHOLD STOCHASTIC VOLATILITY MODELS IN FINANCIAL TIME SERIES

To capture mean and variance asymmetries and time‐varying volatility in financial time series, we generalize the threshold stochastic volatility (THSV) model and incorporate a heavy‐tailed error distribution. Unlike existing stochastic volatility models, this model simultaneously accounts for uncertainty in the unobserved threshold value and in the time‐delay parameter. Self‐exciting and exogenous threshold variables are considered to investigate the impact of a number of market news variables on volatility changes. Adopting a Bayesian approach, we use Markov chain Monte Carlo methods to estimate all unknown parameters and latent variables. A simulation experiment demonstrates good estimation performance for reasonable sample sizes. In a study of two international financial market indices, we consider two variants of the generalized THSV model, with US market news as the threshold variable. Finally, we compare models using Bayesian forecasting in a value‐at‐risk (VaR) study. The results show that our proposed model can generate more accurate VaR forecasts than can standard models.