Towards a material ethnography of linguistic landscape: Multilingualism,mobility and space in a South African township1

The study of multilingual landscapes promises to introduce a new perspective into theories and policies of multilingualism, and to provide essential data for a politics of language. However, the theorization of space and language underlying the notion of linguistic landscape is not able to capture the manifold complexities of (transnational) multilingual mobility that is characteristic of many late‐modern multilingual societies. Basing our argument on signage data from a contemporary South Africa in a dynamic phase of social transformation, we argue that more refined notions of space coupled to a material ethnography of multilingualism could provide a theoretically more relevant and methodologically refocused notion of (multilingual) linguistic landscape. Specifically, we take an approach to landscapes as semiotic moments in the social circulation of discourses (in multiple languages), and view signs as re‐semiotized, socially invested distributions of multilingual resources, the material, symbolic and interactional artifacts of a sociolinguistics of mobility.     

Bayes and empirical Bayes shrinkage estimation of regression coefficients

For a moderate or large number of regression coefficients, shrinkage estimates towards an overall mean are obtained by Bayes and empirical Bayes methods. For a special case, the Bayes and empirical Bayes shrinking weights are shown to be asymptotically equivalent as the amount of shrinkage goes to zero. Based on comparisons between Bayes and empirical Bayes solutions, a modification of the empirical Bayes shrinking weights designed to guard against unreasonable overshrinking is suggested. A numerical example is given.     

Bayesian break-point forecasting in parallel time series,with application to university admissions

A regular supply of applicants to Queen's University in Kingston, Ontario is provided by 65 high schools. Each high school can be characterized by a series of grading standards which change from year to year. To aid admissions decisions, it is desirable to forecast the current year's grading standards for all 65 high schools using grading standards estimated from past year's data. We develop and apply a Bayesian break-point time-series model that generates forecasts which involve smoothing across time for each school and smoothing across schools. “Break point” refers to a point in time which divides the past into the “old past” and the “recent past” where the yearly observations in the recent past are exchangeable with the observations in the year to be forecast. We show that this model works fairly well when applied to 11 years of Queen's University data. The model can be applied to other data sets with the parallel time-series structure and short history, and can be extended in several ways to more complicated structures.     

Bayesian shrinkage estimates for regression coefficients in m populations

For a linear regression model over m populations with separate regression coefficients but a common error variance, a Bayesian model is employed to obtain regression coefficient estimates which are shrunk toward an overall value. The formulation uses Normal priors on the coefficients and diffuse priors on the grand mean vectors, the error variance, and the between-to-error variance ratios. The posterior density of the parameters which were given diffuse priors is obtained. From this the posterior means and variances of regression coefficients and the predictive mean and variance of a future observation are obtained directly by numerical integration in the balanced case, and with the aid of series expansions in the approximately balanced case. An example is presented and worked out for the case of one predictor variable. The method is an extension of Box & Tiao's Bayesian estimation of means in the balanced one-way random effects model.     

Using complexity theory in kinship practice

This reflective discussion draws on data from a collaborative enquiry into kinship or family and friends practice. It introduces perspectives from complexity and chaos theory as a way of re‐examining the challenges of kinship practice. The discussion highlights the enduring challenges for practitioners, not least, managing anxiety and uncertainty, sustaining continuity and containment, becoming experienced and taking account of the complex developmental needs of the children and their families. The discussion particularly raises questions about appropriate settings for practitioners to undertake such a demanding area of child and family social work.     

Proximity (Mis)perception: Public Awareness of Nuclear,Refinery, and Fracking Sites

Whether on grounds of perceived safety, aesthetics, or overall quality of life, residents may wish to be aware of nearby energy sites such as nuclear reactors, refineries, and fracking wells. Yet people are not always accurate in their impressions of proximity. Indeed, our data show that only 54% of Americans living within 25 miles of a nuclear site say they do, and even fewer fracking-proximal (30%) and refinery-proximal (24%) residents respond accurately. In this article, we analyze factors that could either help people form more accurate perceptions or distort their impressions of proximity. We evaluate these hypotheses using a large national survey sample and corresponding geographic information system (GIS) data. Results show that among those living in close proximity to energy sites, those who perceive greater risk are less likely to report living nearby. Conversely, social contact with employees of these industries increases perceived proximity regardless of actual distance. These relationships are consistent across each site type we examine. Other potential factors—such as local news use—may play a role in proximity perception on a case-by-case basis. Our findings are an important step toward a more generalizable understanding of how the public forms perceptions of proximity to risk sites, showing multiple potential mechanisms of bias.     

Rinkeby Swedish and semilingualism in language ideological debates: A Bourdieuean perspective

The focus of this paper is on the mechanisms whereby liberal and well‐meaning democratic societies propagate a cycle of disposession where immigrants are constructed as least resourced while the powerful retain their power. Specifically discussed is the semiotic management of traditional hierarchies of privilege and access through language ideological discourses pertaining to second language acquisition, multilingualism and heterogeneity. One notion in particular is discussed in this context, namely Rinkeby Swedish , a potential, imagined, pan‐immigrant contact variety of Swedish. The discussion is framed within a primarily Bourdieuean conceptual apparatus using concepts of symbolic market, explaining the role of language boundaries and their institutional policing, and detailing the semiotic processes of iconization whereby immigrants are positioned as outside of a symbolically reconstituted community of 'real' Swedish speakers, in strategic attempts to restrict their access to important linguistic and symbolic resources.     

Optimal sampling times in population pharmacokinetic studies

We propose a simulation-based approach to decision theoretic Bayesian optimal design. The underlying probability model is a population pharmacokinetic model which allows for correlated responses (drug concentrations) and patient-to-patient heterogeneity. We consider the problem of choosing sampling times for the anticancer agent paclitaxel, using criteria related to the total area under the curve, the time above a critical threshold and the sampling cost.     

A spatiotemporal model for Mexico City ozone levels

Summary.  We consider hourly readings of concentrations of ozone over Mexico City and propose a model for spatial as well as temporal interpolation and prediction. The model is based on a time-varying regression of the observed readings on air temperature. Such a regression requires interpolated values of temperature at locations and times where readings are not available. These are obtained from a time-varying spatiotemporal model that is coupled to the model for the ozone readings. Two location-dependent harmonic components are added to account for the main periodicities that ozone presents during a given day and that are not explained through the covariate. The model incorporates spatial covariance structure for the observations and the parameters that define the harmonic components. Using the dynamic linear model framework, we show how to compute smoothed means and predictive values for ozone. We illustrate the methodology on data from September 1997.