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This is a work on "hostile" data and the conditions under which they are accepted and rejected. What is the place of data in politics and organization? Why are politicians and administrators so often hostile to research data, or why do they tend to perceive data as hostile to them? How can data become relevant to policy? These questions are the focus of this book. In answer I try to show how political and administrative institutions cope with "hostile" data; how they seek to maintain closedness to disconfirming data, and how they are led, in a free society, to change their policies despite the epistemological bias in favor of the already known and the initial inclination to resist change. At the same time, I demonstrate that data producers must learn that while their research findings may be subjected to science's own standards of verifiability, such data must also meet standards of contestability by the various interests involved in political and administrative decisions. The production and "appropriate" publication of a research report may at best buy one an admission ticket to participate in political and administrative contests, but not the power nor the justification to determine the outcomes of the contest. I begin with two hypotheses: Hypothesis 1: Politicians or administrators reject data that do not coincide with behavior they are unwilling to change. Hypothesis II: Politicians or administrators change behavior that does not coincide with data they are unwilling to reject.
During the 2008 election season, politicians from both sides of the aisle promised to rid government of lobbyists’ undue influence. For the authors of Lobbying and Policy Change, the most extensive study ever done on the topic, these promises ring hollow—not because politicians fail to keep them but because lobbies are far less influential than political rhetoric suggests. Based on a comprehensive examination of ninety-eight issues, this volume demonstrates that sixty percent of recent lobbying campaigns failed to change policy despite millions of dollars spent trying. Why? The authors find that resources explain less than five percent of the difference between successful and unsuccessful efforts. Moreover, they show, these attempts must overcome an entrenched Washington system with a tremendous bias in favor of the status quo. Though elected officials and existing policies carry more weight, lobbies have an impact too, and when advocates for a given issue finally succeed, policy tends to change significantly. The authors argue, however, that the lobbying community so strongly reflects elite interests that it will not fundamentally alter the balance of power unless its makeup shifts dramatically in favor of average Americans’ concerns.
This book efficiently contributes to our understanding of the interplay between data, technology and communicative practice on the one hand, and democratic participation on the other. It addresses the emergence of proactive data activism, a new sociotechnical phenomenon in the field of action that arises as a reaction to massive datafication, and makes affirmative use of data for advocacy and social change. By blending empirical observation and in-depth qualitative interviews, Gutiérrez brings to the fore a debate about the social uses of the data infrastructure and examines precisely how people employ it, in combination with other technologies, to collaborate and act for social change.
An explosion of information has created a crisis for today's information age. It has to be determined how to use the best available information sources, tools, and technology. To do this it is necessary to have leadership at the interagency level to promote a coherent information policy. It is also important to find ways to educate the users of information regarding the tools available to them. Advances in technology resulted in efforts to shift from Disciplinary and Mission-oriented Systems to Decision Support Systems and Personalized Information Systems. One such effort is being made by the Interagency Working Group on Data Management for Global Change (IAWGDMGC). Five federal agencies - the Department of Commerce (DOC), Department of Energy (DOE), National Aeronautics and Space Administration (NASA), National Library of Medicine (NLM), and Department of Defense (DOD) - have an on-going cooperative information management group, CENDI (Commerce, Energy, NASA, NLM, and Defense Information), that is meeting the challenge of coordinating and integrating their information management systems. Although it is beginning to be technically feasible to have a system with text, bibliographic, and numeric data online for the user to manipulate at the user's own workstation, it will require national recognition that the resource investment in such a system is worthwhile, in order to promote its full development. It also requires close cooperation between the producers and users of the information - that is, the research and policy community, and the information community. National resources need to be mobilized in a coordinated manner to move people into the next generation of information support systems. Carroll, Bonnie C. and Jack, Robert F. and Cotter, Gladys A. Unspecified Center NASA-TM-105137, NAS 1.15:105137 ...
Climate change mechanisms, impacts, risks, mitigation, adaption, and governance are widely recognized as the biggest, most interconnected problem facing humanity. Big Data Mining for Climate Change addresses one of the fundamental issues facing scientists of climate or the environment: how to manage the vast amount of information available and analyse it. The resulting integrated and interdisciplinary big data mining approaches are emerging, partially with the help of the United Nation's big data climate challenge, some of which are recommended widely as new approaches for climate change research. Big Data Mining for Climate Change delivers a rich understanding of climate-related big data techniques and highlights how to navigate huge amount of climate data and resources available using big data applications. It guides future directions and will boom big-data-driven researches on modeling, diagnosing and predicting climate change and mitigating related impacts. This book mainly focuses on climate network models, deep learning techniques for climate dynamics, automated feature extraction of climate variability, and sparsification of big climate data. It also includes a revelatory exploration of big-data-driven low-carbon economy and management. Its content provides cutting-edge knowledge for scientists and advanced students studying climate change from various disciplines, including atmospheric, oceanic and environmental sciences; geography, ecology, energy, economics, management, engineering, and public policy. - Provides a step-by-step guide for applying big data mining tools to climate and environmental research - Presents a comprehensive review of theory and algorithms of big data mining for climate change - Includes current research in climate and environmental science as it relates to using big data algorithms