Gendered Morality Tales: Discourses of Gender,Labour, and Value in Globalising Asia

Across Asia, the pursuit of national and global capital accumulation has relied on flexible, feminised work forces and patriarchal models of social reproduction. These gendered patterns of production and reproduction, while central to Asia’s neoliberal ‘miracle’ economies, have also generated powerful discourses that devalue women and their work. Drawing on case studies from across the region, this paper examines the links between these globalising dynamics and provocative local depictions of Asian women as active, desiring, and immoral. These ‘gendered morality tales’ reveal the complex cultural and ideological work that sustains neoliberal models of national economic development. At the same time, these moralising narratives offer insight into the localised negotiations through which marginalised and gendered citizens confront their subordination within the region’s hierarchically ordered political economies.     

The Morality of Divestment

Divestment from companies that produce or heavily utilize fossil fuels has become one of the biggest issues in the contemporary moral debate surrounding climate change. Universities and other institutions are being asked to divest themselves of their fossil fuel–related investments. In this article I argue that, while the case for divestment is morally strong, not all of the arguments used to support divestment are equally strong. Moreover, which of these arguments is employed matters a great deal for the strength of the conclusions regarding divestment. There are two major groups of arguments: what I call positive arguments for divestment, which stem from the claim that divestment is a response to general duties to take action to prevent dangerous climate change, and what I call negative arguments, which stem from a duty not to cause harm. In this article I will briefly characterize what divestment means and to whom it applies. I will then look at negative and positive duty accounts of the duty to divest before looking at the standard objections to both. Objections have tended to cohere around the claims that divesting is inconsistent, useless and/or harmful, or should be abandoned for better options to avoid dangerous climate change.     

Rapid economic development in Singapore and the future of the PAP

The economic development of Singapore has been such that it is frequently cited as a model for capitalist development. One of the most striking features of the Singapore development process has been its distributional consequences. Rapid economic growth has produced remarkably equitable outcomes and the average Singaporean has had little cause for complaint — or at least until recently. This article examines the latest trends with respect to inequality in Singapore, and attempts some assessment of the policy responses and the potential for social instability.     

Conference report

The apologetic literature on capitalist development has been quite unrelenting in its advocacy of free markets to produce “the right conditions” for economic growth. Invariably, however, the only freedom preserved under so-called free-market policies is the economic freedom of international capital to sell, invest, and repatriate profit. Furthermore, “a favourable investment climate” is created only with the systematic repression of unruly students, trade unionists, the free press, and other democratic processes. Dubbed as a necessary precondition for industrialisation, and vindicated by outsiders in the West, many states in the Third World readily accede to the stern government that is necessary to implement these market-oriented policies. It is this issue that provides the focus of debate in this paper, and special attention is devoted to the experience of Singapore under Lee Kuan Yew.     

A Comparison of Logistic Regression,Classification and Regression Tree,and Neural Networks Models in Predicting Violent Re-Offending

Previous studies that have compared logistic regression (LR), classification and regression tree (CART), and neural networks (NNs) models for their predictive validity have shown inconsistent results in demonstrating superiority of any one model. The three models were tested in a prospective sample of 1225 UK male prisoners followed up for a mean of 3.31 years after release. Items in a widely-used risk assessment instrument (the Historical, Clinical, Risk Management-20, or HCR-20) were used as predictors and violent reconvictions as outcome. Multi-validation procedure was used to reduce sampling error in reporting the predictive accuracy. The low base rate was controlled by using different measures in the three models to minimize prediction error and achieve a more balanced classification. Overall accuracy of the three models varied between 0.59 and 0.67, with an overall AUC range of 0.65–0.72. Although the performance of NNs was slightly better than that of LR and CART models, it did not demonstrate a significant improvement.