An agenda for research on work and class in the postsocialist world

This article reviews the scholarly treatment of work and class in postsocialist states. It traces how class discourses under socialism led to a lack of meaningful working class studies in the postsocialist academy. It offers as an agenda for future research three points of departure: (a) greater confrontation of the one‐sided discourse on class in these societies and the academy itself (class blindness of research). (b) The value in studying postsocialist societies both comparatively to global North and South, and as an intermediate positioning for worker exploitation and responses in global capitalism. (c) To achieve the first 2 agenda items, a more grounded methodological approach proceeding from the lived experience of class and work is proposed. Current research on social networks, memory studies and personhood, the informal economy, deindustrialization, and the “domestication” of neoliberalism show that empirically grounded work on postsocialist working classes can make important contributions to wider social science debates. Studying the “losers” of postcommunist transition can tell us much about populist politics, the rise of the global working class outside the global North and the nature of global capitalist exploitation more generally. In addition, this agenda serves as an important point of departure from the dominant middle class focus of research in postsocialism.     

Diversity,Macrosociology, and Religious Belonging: Using Mixed-Level Models in Examining Spatial Variation and the Closed Community Thesis

Abstract

In the past decade, a number of researchers have been interested in both the predictors and causal effects of civic involvement (Blanchard 2007; Funk 1998; Putnam 1993; Putnam 2000; Tolbert, Lyson, and Irwin 1998). Considerable attention has been paid to the effect that religious involvement has on the development of closed and tight-knit communities (Blanchard 2007; Iannaconne 1988, 1994; Porter and Brown 2008; Putnam 1993; Wuthnow 2002). The diversity of friendships gives us a good proxy for the degree of closeness created by existing in-group dynamics formed as a consequence of closed social communities. Data from the 2000 Social Capital Benchmark Survey, concerning individuals from 41 localities across the United States, was used in this analysis (Roper 2000). Furthermore, local-level variables concerning the rate of religious adherence were introduced as a way of better understanding potential multilevel effects. My findings support a number of these, showing that religious group membership significantly lowers an individual's diversity of friendships when compared to the effect of belonging to other groups.     

Review and implementation of cure models based on first hitting times for Wiener processes

The development of models and methods for cure rate estimation has recently burgeoned into an important subfield of survival analysis. Much of the literature focuses on the standard mixture model. Recently, process-based models have been suggested. We focus on several models based on first passage times for Wiener processes. Whitmore and others have studied these models in a variety of contexts. Lee and Whitmore (Stat Sci 21(4):501–513, 2006) give a comprehensive review of a variety of first hitting time models and briefly discuss their potential as cure rate models. In this paper, we study the Wiener process with negative drift as a possible cure rate model but the resulting defective inverse Gaussian model is found to provide a poor fit in some cases. Several possible modifications are then suggested, which improve the defective inverse Gaussian. These modifications include: the inverse Gaussian cure rate mixture model; a mixture of two inverse Gaussian models; incorporation of heterogeneity in the drift parameter; and the addition of a second absorbing barrier to the Wiener process, representing an immunity threshold. This class of process-based models is a useful alternative to the standard model and provides an improved fit compared to the standard model when applied to many of the datasets that we have studied. Implementation of this class of models is facilitated using expectation-maximization (EM) algorithms and variants thereof, including the gradient EM algorithm. Parameter estimates for each of these EM algorithms are given and the proposed models are applied to both real and simulated data, where they perform well.     

Estimating Risk Assessment Exposure Point Concentrations when the Data Are Not Normal or Lognormal

The U.S. Environmental Protection Agency (EPA) recommends the use of the one-sided 95% upper confidence limit of the arithmetic mean based on either a normal or lognormal distribution for the contaminant (or exposure point) concentration term in the Superfund risk assessment process. When the data are not normal or lognormal this recommended approach may overestimate the exposure point concentration (EPC) and may lead to unecessary cleanup at a hazardous waste site. The EPA concentration term only seems to perform like alternative EPC methods when the data are well fit by a lognormal distribution. Several alternative methods for calculating the EPC are investigated and compared using soil data collected from three hazardous waste sites in Montana, Utah, and Colorado. For data sets that are well fit by a lognormal distribution, values for the Chebychev inequality or the EPA concentration term may be appropriate EPCs. For data sets where the soil concentration data are well fit by gamma distributions, Wong's method may be used for calculating EPCs. The studentized bootstrap-t and Hall's bootstrap-t transformation are recommended for EPC calculation when all distribution fits are poor. If a data set is well fit by a distribution, parametric bootstrap may provide a suitable EPC.     

Durkheim and the Homo Duplex: Anthropocentrism in Sociology

Previous analyses of anthropocentrism in sociological theory primarily attribute the origins of anthropocentrism in sociology to George Herbert Mead. This study addresses anthropocentrism in the influential works of David Émile Durkheim. At the core of Durkheim’s theory is his concept of the homo duplex, an inherent but tentative quality separating humans from all other animals. Durkheim uses the homo duplex as an ontological device, defining humanity as having the unique capacity to create and participate in the social. This collective process permits humans to transcend the profane, or what he observes as the immoral, passionate, animalistic individualism of nonhuman animals, into social solidarity: a realm generating morality and, ultimately, the sacred. This key distinction serves as the basis of all Durkheimian theory. This profound anthropocentrism becomes significant considering the degree of Durkheim’s influence on the field of sociology and the extent of anthropocentrism in sociology as a whole.     

Research note: imputing large group averages for missing data,using rural-urban continuum codes for density driven industry sectors

Understanding the effects and consequences of missing data imputation is vital to the ability to obtain meaningful and reliable statistics and coefficients in the examination of any quantitatively-based phenomena. Over time a series of sophisticated methods have been developed to handle the issue of missing data imputation however, these sophisticated methods may not always be appropriate or attainable. In these specific cases more traditional approaches to missing data imputation must be employed and driven by the research project, theoretical framework, and the data. In this research note we offer a brief account of one such instance, implementing a large-group mean imputation approach to handling missing data. The analysis is drawn from a much larger project and shows the effect of proper group selection in terms of mean imputation using a cross-validation approach based on the imputed data’s relation to known values. Ultimately, the results show that the use of Rural-Urban Continuum codes are superior to currently used group-means in the U.S., thus introducing a new, and more efficient, approach to the handling of missing data using group-mean imputation.