Beyond recruitment: Predictors of differential participation in a national antihunger organization

Surprisingly little research has sought to explain differential participation after recruitment into social movement organizations (SMOs). This study examines the influence of several sets of predictors on participation by members of a national organization in the antihunger movement. The findings highlight the importance of incentive, ideological, and microstructural factors for differential participation and suggest that favorable perceptions of SMOs also promote participation. Final remarks address the implications of the findings for the social movement and voluntary organization literatures.     

Female Labor Force Participation in Urban and Rural China1

Abstract The objective of this study is to examine female labor force participation and its determinants in rural and urban China. The sociological literature has demonstrated that participation tends to increase in urban and industrialized places where women have higher levels of education and fewer children, where more workers are engaged in service pursuits, and where family structure is less traditional. With the use of data on counties and cities (N = 2,377) from the I-percent sample of the 1982 census of the People's Republic of China, it was found that female labor force participation is likely to rise in areas with increased agricultural employment, educational levels, proportion of female-headed households, and higher male-to-female sex ratios. Both the size of the service sector and the fertility rate had negligible effects on female labor force participation. Although, on average, rural places have slightly higher levels of female labor force participation, when other variables are controlled, urban places have a higher rate of female participation. In addition, the findings suggest that market factors (i.e., education) are more likely to determine the rate of female labor force participation in urban areas; whereas demographic and social factors (i.e., sex ratio and household structure) play a more important role in explaining the female labor force participation in rural counties.     

Tree health mapping with multispectral remote sensing data at UC Davis, California

Tree health is a critical parameter for evaluating urban ecosystem health and sustainability. Traditionally, this parameter has been derived from field surveys. We used multispectral remote sensing data and GIS techniques to determine tree health at the University of California, Davis. The study area (363 ha) contained 8,962 trees of 215 species. Tree health conditions were mapped for each physiognomic type at two scales: pixel and whole tree. At the pixel scale, each tree pixel within the tree crown was classified as either healthy or unhealthy based on vegetation index values. At the whole tree scale, raster based statistical analysis was used to calculate tree health index which is the ratio of healthy pixels to entire tree pixels within the tree crown. The tree was classified as healthy if the index was greater than 70%. Accuracy was checked against a random sample of 1,186 trees. At the whole tree level, 86% of campus trees were classified as healthy with 88% mapping accuracy. At the pixel level, 86% of the campus tree cover was classified as healthy. This tree health evaluation approach allows managers to identify the location of unhealthy trees for further diagnosis and treatment. It can be used to track the spread of disease and monitor seasonal or annual changes in tree health. Also, it provides tree health information that is fundamental to modeling and analysis of the environmental, social, and economic services produced by urban forests.     

Opposition to Solid Waste Incineration: Pre- Implementation Anxieties Surrounding a New Environmental Controversy*

Despite sociopolitical controversy surrounding the solid waste crisis, little sociological attention has focused on this crucial environmental issue. Drawing on research from the technological controversies, environmental concern, and solid waste literatures, this study examines the pre-implementation anxieties of a Louisiana community faced with a proposal to build an incinerator in its backyard. Utilizing survey data, a causal path model is developed in which the perceived advantages of the technology, general environmental concern, and support for recycling initiatives are identified as explanatory variables of incinerator opposition. Implications of these findings for the study of technical controversies and the larger solid waste crisis also are suggested.     

Ninth-Grade Students' Attitudes Toward Nontraditional Occupations

This study explored how students choose careers, their attitudes toward vocational education, and whether or not they would consider enrolling in a nontraditional vocational education program.     

Domains of convergence for the EM algorithm: a cautionary tale in a location estimation problem

The EM algorithm is a popular method for maximizing a likelihood in the presence of incomplete data. When the likelihood has multiple local maxima, the parameter space can be partitioned into domains of convergence, one for each local maximum. In this paper we investigate these domains for the location family generated by the t-distribution. We show that, perhaps somewhat surprisingly, these domains need not be connected sets. As an extreme case we give an example of a domain which consists of an infinite union of disjoint open intervals. Thus the convergence behaviour of the EM algorithm can be quite sensitive to the starting point.     

Modelling map positional error to infer true feature location

The authors consider the issue of map positional error, or the difference between location as represented in a spatial database (i.e., a map) and the corresponding unobservable true location. They propose a fully model‐based approach that incorporates aspects of the map registration process commonly performed by users of geographic informations systems, including rubber‐sheeting. They explain how estimates of positional error can be obtained, hence estimates of true location. They show that with multiple maps of varying accuracy along with ground truthing data, suitable model averaging offers a strategy for using all of the maps to learn about true location.     

SUNK COSTS, PROFIT VARIABILITY, AND TURNOVER

Dynamic competitive models of industry evolution suggest that firm profit will be more volatile, and turnover lower, in industries with higher sunk costs. These implications are consistent with empirical observation. (JEL L00 )