In 2014, Mia Love made history by becoming the first black Republican woman elected to Congress, and she accomplished this feat in Utah.The story of how she did it begins in Port-au-Prince, Haiti, during the reign of "Papa Doc" Duvalier, one of the world's most reviled dictators. It continues in suburban Connecticut, where she reveled in musical theater. And after her conversion to Mormonism it shifts to Utah, where a political awakening led her first to the city council in Saratoga Springs and then the halls of Congress.In this political biography, Salt Lake Tribune reporters Matt Canham, Robert Gehrke and Thomas Burr explore the defining moments in her life, illuminated through dozens of interviews with Mia, her family, those closest to her and those critical of her.She's dynamic, comfortable in the spotlight, and if you're interested in politics, you're likely to see a lot of her in the years to come. But Mia's political success wasn't preordained.During her first congressional campaign, she was forced to replace key staff members, spent too much time campaigning for GOP presidential candidate Mitt Romney, and her polls were flawed. Still, she lost to a well-known Democratic incumbent by three tenths of one percent.In less than two months, she returned to the campaign trail and marched to victory. She'll take the oath of office in January 2015, joining a House Republican caucus that wants to increase its appeal to minorities and women. Mia Love says she aims to be front and center in Washington, representing her state and her party, as the next GOP star.
The real-world data, though massive, is largely unstructured, in the form of natural-language text. It is challenging but highly desirable to mine structures from massive text data, without extensive human annotation and labeling. In this book, we investigate the principles and methodologies of mining structures of factual knowledge (e.g., entities and their relationships) from massive, unstructured text corpora. Departing from many existing structure extraction methods that have heavy reliance on human annotated data for model training, our effort-light approach leverages human-curated facts stored in external knowledge bases as distant supervision and exploits rich data redundancy in large text corpora for context understanding. This effort-light mining approach leads to a series of new principles and powerful methodologies for structuring text corpora, including (1) entity recognition, typing and synonym discovery, (2) entity relation extraction, and (3) open-domain attribute-value mining and information extraction. This book introduces this new research frontier and points out some promising research directions.
Various explanations have been put forward as to why the Keynesian Revolution in economics in the 1930s and 1940s took place. Some of these point to the temporal relevance of John Maynard Keynes's The General Theory of Employment, Interest, and Money (1936), appearing, as it did, just a handful of years after the onset of the Great Depression, whilst others highlight the importance of more anecdotal evidence, such as Keynes’s close relations with the Cambridge ‘Circus’, a group of able, young Cambridge economists who dissected and assisted Keynes in developing crucial ideas in the years leading up to the General Theory. However, no systematic effort has been made to bring together these and other factors to examine them from a sociology of science perspective. This book fills this gap by taking its cue from a well-established tradition of work from history of science studies devoted to identifying the intellectual, technical, institutional, psychological and financial factors which help to explain why certain research schools are successful and why others fail. This approach, it turns out, provides a coherent account of why the revolution in macroeconomics was ‘Keynesian’ and why, on a related note, Keynes was able to see off contemporary competitor theorists, notably Friedrich von Hayek and Michal Kalecki.
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