Class,race, and the disposal of urban waste: Locations of landfills,incinerators, and sewage treatment plants

This study compared the socioeconomic status, racial composition, and ethnic composition of 49 randomly selected U.S. cities with those of the census tracts containing the solid waste disposal facilities and sewage treatment plants for those cities. Contrary to the environmental racism and classism hypotheses, residents of tracts with landfills or incinerators had higher incomes and were less likely to be minority group members than residents of the cities that generated the refuse—although they did have somewhat lower education levels. There were few differences between the population characteristics of the cities and the census tracts that contained their sewage treatment plants. Possible explanations for these findings are discussed.     

Changing Workplaces to Reduce Work-Family Conflict: Schedule Control in a White-Collar Organization

Work-family conflicts are common and consequential for employees, their families, and work organizations. Can workplaces be changed to reduce work-family conflict? Previous research has not been able to assess whether workplace policies or initiatives succeed in reducing work-family conflict or increasing work-family fit. Using longitudinal data collected from 608 employees of a white-collar organization before and after a workplace initiative was implemented, we investigate whether the initiative affects work-family conflict and fit, whether schedule control mediates these effects, and whether work demands, including long hours, moderate the initiative's effects on work-family outcomes. Analyses clearly demonstrate that the workplace initiative positively affects the work-family interface, primarily by increasing employees' schedule control. This study points to the importance of schedule control for our understanding of job quality and for management policies and practices.     

Racialization in a ‘homogenous’ society? The case of Buraku people in Japan

This paper examines the puzzling status of Buraku people in Japan through the methodological prism of historical sociology. I develop a theoretical approach that emphasizes the concept of racialization to illuminate the historical and social construction of Buraku status. I propose this approach as a complementary alternative to prior analytic perspectives. Two major perspectives have dominated research on the status of Buraku people. First, an earlier approach that focused on the legacy of caste discrimination. The second framework focuses on Buraku people as part of the struggle for universal human rights. This article raises three basic, but interrelated questions about the historical development of Buraku status in Japan. Why did the Buraku people become subject to such overt discrimination in local community life, and through government policy? How was the state involved in the social construction of Buraku people? How did the Buraku liberation struggles challenge formalized discrimination?     

Studying the dependence between ordinal-nominal categorical variables via orthogonal polynomials

In situations where the structure of one of the variables of a contingency table is ordered recent theory involving the augmentation of singular vectors and orthogonal polynomials has shown to be applicable for performing symmetric and non-symmetric correspondence analysis. Such an approach has the advantage of allowing the user to identify the source of variation between the categories in terms of components that reflect linear, quadratic and higher-order trends. The purpose of this paper is to focus on the study of two asymmetrically related variables cross-classified to form a two-way contingency table where only one of the variables has an ordinal structure.     

k-POD: A Method for k-Means Clustering of Missing Data

The k-means algorithm is often used in clustering applications but its usage requires a complete data matrix. Missing data, however, are common in many applications. Mainstream approaches to clustering missing data reduce the missing data problem to a complete data formulation through either deletion or imputation but these solutions may incur significant costs. Our k-POD method presents a simple extension of k-means clustering for missing data that works even when the missingness mechanism is unknown, when external information is unavailable, and when there is significant missingness in the data.

[Received November 2014. Revised August 2015.]