Assimilation as multiracialism: The case of Singapore's Malays

The myths of meritocracy and multiracialism ‘explain’ between them both the ‘fairness’ of the Singapore system and the subordinate role of the non-Chinese minority races. They also purport to assure the minorities that they enjoy full status as members of the nation-building project and that their cultural and religious mores are embraced and protected within its framework. Using the Malay minority as its case study, and arguing from archival, oral, official government and secondary sources, this paper argues that the Singapore systems of meritocracy and multiracialism have not been concerned primarily with intercommunal tolerance since the 1970s, but are now programmes of assimilation of the racial minorities into a Chinese-dominated society.     

Divergence-based estimation and testing with misclassified data

The well-known chi-squared goodness-of-fit test for a multinomial distribution is generally biased when the observations are subject to misclassification. In Pardo and Zografos (2000) the problem was considered using a double sampling scheme and ø-divergence test statistics. A new problem appears if the null hypothesis is not simple because it is necessary to give estimators for the unknown parameters. In this paper the minimum ø-divergence estimators are considered and some of their properties are established. The proposed ø-divergence test statistics are obtained by calculating ø-divergences between probability density functions and by replacing parameters by their minimum ø-divergence estimators in the derived expressions. Asymptotic distributions of the new test statistics are also obtained. The testing procedure is illustrated with an example.     

Marginal and association regression models for longitudinal binary data with drop‐outs: A likelihood‐based approach

Longitudinal data often contain missing observations, and it is in general difficult to justify particular missing data mechanisms, whether random or not, that may be hard to distinguish. The authors describe a likelihood‐based approach to estimating both the mean response and association parameters for longitudinal binary data with drop‐outs. They specify marginal and dependence structures as regression models which link the responses to the covariates. They illustrate their approach using a data set from the Waterloo Smoking Prevention Project They also report the results of simulation studies carried out to assess the performance of their technique under various circumstances.     

Culturalising the Abject: Islam,Law and Moral Panic in the West

This paper explores the construction of Islam as abject and the symbolic positioning of Muslims as being outside secular modernity in Australia through an analysis of the way the criminal legal process and perceptions of criminality are culturalised. The empirical focus is gang rape and the trials of Muslim youth on gang rape charges in Sydney between 2000–2003 which quickly became culturally inflected as ‘Muslim’ and ‘Lebanese’ by media reporting of the criminal trials and moral panic about them as source of social menace. Three dimensions of culturalisation of crime and the criminalisation of culture are identified in the criminal legal process and media reporting of it; firstly the cultural inflection of new laws against gang rape by their association with particular events and trials; secondly the introduction of ‘cultural defence’ by the accused as a mitigating factor in the criminal legal process; thirdly, media reporting and commentary on criminal cases which emphasize cultural explanations for individual criminal behaviour.     

Causal inference with generalized structural mean models

Summary.  We estimate cause–effect relationships in empirical research where exposures are not completely controlled, as in observational studies or with patient non-compliance and self-selected treatment switches in randomized clinical trials. Additive and multiplicative structural mean models have proved useful for this but suffer from the classical limitations of linear and log-linear models when accommodating binary data. We propose the generalized structural mean model to overcome these limitations. This is a semiparametric two-stage model which extends the structural mean model to handle non-linear average exposure effects. The first-stage structural model describes the causal effect of received exposure by contrasting the means of observed and potential exposure-free outcomes in exposed subsets of the population. For identification of the structural parameters, a second stage 'nuisance' model is introduced. This takes the form of a classical association model for expected outcomes given observed exposure. Under the model, we derive estimating equations which yield consistent, asymptotically normal and efficient estimators of the structural effects. We examine their robustness to model misspecification and construct robust estimators in the absence of any exposure effect. The double-logistic structural mean model is developed in more detail to estimate the effect of observed exposure on the success of treatment in a randomized controlled blood pressure reduction trial with self-selected non-compliance.     

Family Cap and Nonmarital Fertility: The Racial Conditioning of Policy Effects

Using an experimental design, this research examines the effect of the nation's first family cap on the births, abortions, and contraception use of over 8,000 women receiving public assistance in New Jersey. The family cap denies additional cash benefits to children conceived while the mother is receiving public assistance. Our research shows that a targeted welfare benefit manipulation does influence fertility behavior; however, the effect is conditioned by race. We find that Black women in the experimental group have a 21% lower birth rate and a 32% higher abortion rate than Black women in the control group. We do not find a birth effect for Hispanic or White women. We discuss the policy implications of the effects of a segmented family cap.     

Boundary Spaces

While shows like The X-Files and 24 have merged conspiracy theories with popular science (fictions), some video games have been pushing the narrative even further. Electronic Art's Majestic game was released in July 2001 and quickly generated media buzz with its unusual multi-modal gameplay. Mixing phone calls, faxes, instant messaging, real and "fake' websites, and email, the game provides a fascinating case of an attempt at new directions for gaming communities. Through story, mode of playing, and use of technology, Majestic highlights the uncertain status of knowledge, community and self in a digital age; at the same time, it allows examination of alternative ways of understanding games' role and purpose in the larger culture. Drawing on intricate storylines involving government conspiracies, techno-bio warfare, murder and global terror, players were asked to solve mysteries in the hopes of preventing a devastating future of domination. Because the game drew in both actual and Majestic-owned/-designed websites, it constantly pushed those playing the game right to borders where simulation collides with " factuality'. Given the wide variety of "legitimate' conspiracy theory, alien encounters and alternative science web pages, users often could not distinguish when they were leaving the game's pages and venturing into " real' World Wide Web sites. Its further use of AOL's instant messenger system, in which gamers spoke not only to bots but to other players, pushed users to evaluate constantly both the status of those they were talking to and the information being provided. Additionally, the game required players to occupy unfamiliar subject positions, ones where agency was attenuated, and which subsequently generated a multi-layered sense of unease among players. This mix of authentic and staged information in conjunction with technologically mediated roles highlights what are often seen as phenomenon endemic to the Internet itself; that is, the destabilization of categories of knowing, relating, and being.     

Measuring Substance Abuse Treatment Need Among Adults in Florida: A Social Indicators Approach

Determining the size and demographiccharacteristics of substance abuse populationsis extremely important for implementing publicpolicies aimed at the control of substanceabuse. Such information not only assists in theallocation of limited treatment resources bythe state, but also in the monitoring ofsubstance abuse trends over time and in theevaluation of innovative policy initiatives. Inthis study, we develop three composite measuresof treatment need. We then use these measuresto estimate treatment need for alcohol abuseand for controlled substance abuse within eachof Florida's 67 counties. This study providesan important empirical component of communityplanning, quantifying and, to a limited degree,specifying the level of need for the substanceabuse treatment of community residents. Anadditional benefit is the development of a costeffective and unobtrusive methodology fordetermining empirically when levels of need arechanging so that treatment levels can beadjusted accordingly. With proper use,policymakers can readily employ the methodologydeveloped in this study in Florida andelsewhere to make better-informed decisions inthe allocation of finite substance abusetreatment resources.