Class Unknown


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Class Unknown


Class Unknown

Author: Mark Pittenger

language: en

Publisher: NYU Press

Release Date: 2012-08-13


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How well-meaning intellectuals helped develop our understanding of the American underclass Since the Gilded Age, social scientists, middle-class reformers, and writers have left the comforts of their offices to "pass" as steel workers, coal miners, assembly-line laborers, waitresses, hoboes, and other working and poor people in an attempt to gain a fuller and more authentic understanding of the lives of the working class and the poor. In this first, sweeping study of undercover investigations of work and poverty in America, award-winning historian Mark Pittenger examines how intellectuals were shaped by their experiences with the poor, and how despite their sympathy toward working-class people, they unintentionally helped to develop the contemporary concept of a degraded and "other" American underclass. While contributing to our understanding of the history of American social thought, Class Unknown offers a new perspective on contemporary debates over how we understand and represent our own society and its class divisions.

The Elimination of Unknown Ages in the 1940 Population Census


The Elimination of Unknown Ages in the 1940 Population Census

Author: United States. Bureau of the Census

language: en

Publisher:

Release Date: 1942


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Detection and Identification of Rare Audio-visual Cues


Detection and Identification of Rare Audio-visual Cues

Author: Daphna Weinshall

language: en

Publisher: Springer Science & Business Media

Release Date: 2011-10-16


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Machine learning builds models of the world using training data from the application domain and prior knowledge about the problem. The models are later applied to future data in order to estimate the current state of the world. An implied assumption is that the future is stochastically similar to the past. The approach fails when the system encounters situations that are not anticipated from the past experience. In contrast, successful natural organisms identify new unanticipated stimuli and situations and frequently generate appropriate responses. The observation described above lead to the initiation of the DIRAC EC project in 2006. In 2010 a workshop was held, aimed to bring together researchers and students from different disciplines in order to present and discuss new approaches for identifying and reacting to unexpected events in information-rich environments. This book includes a summary of the achievements of the DIRAC project in chapter 1, and a collection of the papers presented in this workshop in the remaining parts.