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.     

Restructuring of the Financial Industry: The Disappearance of Locally Owned Traditional Financial Services in Rural America

Restructuring in the financial services industry has altered the relationship between small business owners and capital. In the past small businesses have relied on relational, or soft data, lending from locally owned banks for capital. The proliferation of absentee‐owned local branch networks brought standardized practices, thus eliminating the autonomy of local loan officers to utilize soft data in loan decisions. In this article we examine the changes in the percentage of traditional financial services that are locally owned in three county types: metropolitan, micropolitan, and noncore. We utilize the Longitudinal Business Database at the U.S. Census Bureau Center for Economic Studies. We examine changes in local ownership of traditional financial services between 1976 and 2007. We find that the rate of decline of local ownership has been greatest in the noncore (most rural) counties. We also explore to what extent these patterns are related to the emergence of alternative financial services during the same period. We find that such alternative services are growing in all three county types, but at rates not significantly different than the population growth for these county types. We supplement our analysis with data from qualitative interviews with small business owners throughout rural Texas. We conclude with a discussion of implications and plans for future research.     

Young People Leaving Care: Participatory Research to Improve Child Welfare Practices and the Rights of Children and Young People

Abstract

This paper discusses participatory research with young people who are leaving public care in Finland to begin independent lives. The aim of the research, organised by SOS Children's Villages International, was to bring about change in alternative care arrangements, particularly those involving young people's transition to independence. The project used a participatory research design based on employing care-leaving peers as co-researchers. This paper adheres to the methodological principles of empowerment in analysing the personal experiences of young people leaving alternative care with the goal of informing good practice. The findings suggest that the peer research method can be an effective means of empowering young people to develop research skills and to be involved in knowledge production, as well as serving as a means of promoting improved services for “care-leavers”, those young people who are leaving either foster care or institutional care. The participatory and peer research method challenges the traditional understandings of expertise and knowledge production. Although the hierarchy between adult researchers and young people as co-researchers is still evident, the method provides possibilities for better understanding the social- and health-service systems and their challenges and pitfalls from a user's perspective.     

A Sociological Alternative to the Psychiatric Conceptualization of Mental Suffering

Uncovering both the structural causes and experiences of suffering is a central sociological endeavor. Sociologists study many different kinds of suffering; after all, strife is experienced both physically and emotionally, because of internal factors such as illness, due to external factors such as trauma, and as a result of economic, political or natural environments. In this paper, I address one form of suffering: mental suffering. In particular, I describe the medicalization of mental suffering in biological psychiatry, which focuses on the genetic factors of illness and equates mental suffering with mental illness. The psychiatric concept of mental illness highlights the continuing, crucial role for sociology in both understanding the experience and identifying the structural roots of suffering. Since the dominant conceptualization of mental suffering is as a medical concept, it is vital for sociology to offer alternative explanations and contribute to a multidimensional analysis. The roots of mental suffering are much more than biological; social comparison, social inequality, and other social stressors are equally important etiological considerations. Therefore, a true understanding of mental suffering requires multiple perspectives, and sociological constructs guard against a total medicalization of mental suffering.     

A perspective of composite sampling

Composite samples are formed by physically mixing samples. Usually, composite samples are used to reduce the overall cost associated with analytical procedures that must be performed on each sample, but they can also be used to protect the privacy of individuals.

Composite sampling can reduce the cost of identifying individual cases that have a certain trait, such as those with a rare disease or those exceeding pollution-level standards. Not much is lost by applying this method as long as the trait is relatively rare.

Composite sampling can reduce the cost of estimating the mean of some process. When samples are composited, the ability to estimate the variance is lost. In spite of this, the potential savings are so great that composite samples have been used.

Much of this paper deasl with the variance of estimators based on composite sampling when the porportions of hte original samples comprising the composite sample are actually random. Taking repeated samples and measurements on several composite samples complicates the prodcedure, but allows the estimation of between and within variation as well as measurement error.     

Identification of composite (∑+II) arma models by relatively simpler models

In the real world situations, many time series are aggregates of two or more time series. An aggregation may take place due to an addition or the product or both of two or more time series. We are often interested in the study of the properties of aggregates which are, in turn, dependent on the properties of the constituent series. Motivated by this problem, the authors study in this paper the properties of models generated by the operator (Σ+II) on autoregressive-moving-average (ARMA) processes of orders (p_i,q_i), i = l→n . A few practical examples where such models have been used are given in the introduction and an illustrative numerical example is discussed at the end of the paper.     

The power functions of the likelihood ratio tests for a simply ordered trend in normal means

Likelihood ratio tests for the homogeneity of k normal means with the alternative restricted by an increasing trend are considered as well as the likelihood ratio tests of the null hypothesis that the means satisfy the trend. While the work is primarily a survey of results concerning the power functions of these tests, the extensions of some results to the case of not necessarily equal sample sizes are presented. For the case of known or unknown population variances, exact expressions are given for the power functions for k=3,4, and approximations are discussed for larger k. The topics of consistency, bias and monotonicity of the power functions are included. Also, Bartholomew's conjectures concerning minimal and maximal powers are investigated, with results of a new numerical study given.     

The exact solution to the mle of a decreasing density based on arbitrarily right censored data

Nonparametric maximum likelihood estimation of decreasing and unimodal density functions, based on observations subject to arbitrary right censorship, was considered by McNichols and Padgett(1982). In order to compute their estimators, however, nonlinear equations with linear constraints had to be maximized using numerical techniques. The exact solution to this problem can now be found. An example illustrates the simplicity of the method.