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Grassroots Advocacy and Health Care Reform shows how grassroots issue advocacy is conducted today by telling the story of Health Care for America Now's campaign in Pennsylvania in support of the Affordable Care Act. As both the director of the HCAN PA campaign and a former professor of political theory, Stier brings a unique perspective to the analysis of how HCAN Pennsylvania implemented a national campaign in the context of contemporary political science. Grassroots Advocacy and Health Care Reform provides valuable insights for historians, political scientists, organizers, and citizens alike about how advocates can best build campaigns to affect legislative change.
Based on a symposium honoring the extensive work of Allen Newell --
one of the founders of artificial intelligence, cognitive science,
human-computer interaction, and the systematic study of
computational architectures -- this volume demonstrates how
unifying themes may be found in the diversity that characterizes
current research on computers and cognition. The subject matter
includes:
Based on a symposium honoring the extensive work of Allen Newell --
one of the founders of artificial intelligence, cognitive science,
human-computer interaction, and the systematic study of
computational architectures -- this volume demonstrates how
unifying themes may be found in the diversity that characterizes
current research on computers and cognition. The subject matter
includes:
Grassroots Advocacy and Health Care Reform places a detailed account of how the Health Care for America Now campaign in Pennsylvania carried out contemporary issue advocacy in the context of an understanding of American politics.
In early 1986, one of us (D.M.S.) was constructing an artificial intelligence system to design algorithms, and the other (A.P.A.) was getting started in program transformations research. We shared an office, and exchanged a few papers on the systematic development of algorithms from specifications. Gradually we realized that we were trying to solve some of the same problems. And so, despite radical differences between ourselves in research approaches, we set out together to see what we could learn from these papers. That's how this book started: a couple of graduate students trying to cope with The Literature. At first, there was just a list of papers. One of us (D.M.S.) tried to cast the papers in a uniform framework by describing the problem spaces searched, an approach used in artificial intelligence for understanding many tasks. The generalized problem space descriptions, though useful, seemed to abstract too much, so we decided to compare papers by different authors dealing with the same algorithm. These comparisons proved crucial: for then we began to see similar key design choices for each algorithm.
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