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In American society, the consumption of alcohol during pregnancy is considered dangerous, irresponsible, and in some cases illegal. Pregnant women who have even a single drink routinely face openly voiced reproach. Yet fetal alcohol syndrome (FAS) in infants and children is notoriously difficult to diagnose, and the relationship between alcohol and adverse birth outcomes is riddled with puzzles and paradoxes. Sociologist Elizabeth M. Armstrong uses fetal alcohol syndrome and the problem of drinking during pregnancy to examine the assumed relationship between somatic and social disorder, the ways in which social problems are individualized, and the intertwining of health and morality that characterizes American society. She traces the evolution of medical knowledge about the effects of alcohol on fetal development, from nineteenth-century debates about drinking and heredity to the modern diagnosis of FAS and its kindred syndromes. She argues that issues of race, class, and gender have influenced medical findings about alcohol and reproduction and that these findings have always reflected broader social and moral preoccupations and, in particular, concerns about women's roles and place in society, as well as the fitness of future generations. Medical beliefs about drinking during pregnancy have often ignored the poverty, chaos, and insufficiency of some women's lives—factors that may be more responsible than alcohol for adverse outcomes in babies and children. Using primary sources and interviews to explore relationships between doctors and patients and women and their unborn children, Armstrong offers a provocative and detailed analysis of how drinking during pregnancy came to be considered a pervasive social problem, despite the uncertainties surrounding the epidemiology and etiology of fetal alcohol syndrome.
Pattern Recognition has a long history of applications to data analysis in business, military and social economic activities. While the aim of pattern recognition is to discover the pattern of a data set, the size of the data set is closely related to the methodology one adopts for analysis. Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery tackles those data sets and covers a variety of issues in relation to intelligent data analysis so that patterns from frequent or rare events in spatial or temporal spaces can be revealed. This book brings together current research, results, problems, and applications from both theoretical and practical approaches.