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An interdisciplinary and quantitative account of human claims on the biosphere's stores of living matter, from prehistoric hunting to modern energy production. The biosphere—the Earth's thin layer of life—dates from nearly four billion years ago, when the first simple organisms appeared. Many species have exerted enormous influence on the biosphere's character and productivity, but none has transformed the Earth in so many ways and on such a scale as Homo sapiens. In Harvesting the Biosphere, Vaclav Smil offers an interdisciplinary and quantitative account of human claims on the biosphere's stores of living matter, from prehistory to the present day. Smil examines all harvests—from prehistoric man's hunting of megafauna to modern crop production—and all uses of harvested biomass, including energy, food, and raw materials. Without harvesting of the biomass, Smil points out, there would be no story of human evolution and advancing civilization; but at the same time, the increasing extent and intensity of present-day biomass harvests are changing the very foundations of civilization's well-being. In his detailed and comprehensive account, Smil presents the best possible quantifications of past and current global losses in order to assess the evolution and extent of biomass harvests. Drawing on the latest work in disciplines ranging from anthropology to environmental science, Smil offers a valuable long-term, planet-wide perspective on human-caused environmental change.
"The Most Powerful Idea in the World argues that the very notion of intellectual property drove not only the invention of the steam engine but also the entire Industrial Revolution." -- Back cover.
Kazu Kibuishi's #1 NEW YORK TIMES bestselling series is now available in eBook! After the tragic death of their father, Emily and Navin move with their mother to the home of her deceased great-grandfather, but the strange house proves to be dangerous. Before long, a sinister creature lures the kids' mom through a door in the basement. Em and Navin, desperate not to lose her, follow her into an underground world inhabited by strange creatures, robots, and talking animals.Eventually, they enlist the help of a small mechanical rabbit named Miskit. Together with Miskit, they face the most terrifying monster of all, and Em finally has the chance to save someone she loves.
The landmark, prize-winning, international bestselling examination of how a gender gap in data perpetuates bias and disadvantages women. #1 International Bestseller * Winner of the Financial Times and McKinsey Business Book of the Year Award * Winner of the Royal Society Science Book Prize Data is fundamental to the modern world. From economic development to health care to education and public policy, we rely on numbers to allocate resources and make crucial decisions. But because so much data fails to take into account gender, because it treats men as the default and women as atypical, bias and discrimination are baked into our systems. And women pay tremendous costs for this insidious bias: in time, in money, and often with their lives. Celebrated feminist advocate Caroline Criado Perez investigates this shocking root cause of gender inequality in Invisible Women. Examining the home, the workplace, the public square, the doctor’s office, and more, Criado Perez unearths a dangerous pattern in data and its consequences on women’s lives. Product designers use a “one-size-fits-all” approach to everything from pianos to cell phones to voice recognition software, when in fact this approach is designed to fit men. Cities prioritize men’s needs when designing public transportation, roads, and even snow removal, neglecting to consider women’s safety or unique responsibilities and travel patterns. And in medical research, women have largely been excluded from studies and textbooks, leaving them chronically misunderstood, mistreated, and misdiagnosed. Built on hundreds of studies in the United States, in the United Kingdom, and around the world, and written with energy, wit, and sparkling intelligence, this is a groundbreaking, highly readable exposé that will change the way you look at the world.
One of the most urgent challenges in African economic development is to devise a strategy for improving statistical capacity. Reliable statistics, including estimates of economic growth rates and per-capita income, are basic to the operation of governments in developing countries and vital to nongovernmental organizations and other entities that provide financial aid to them. Rich countries and international financial institutions such as the World Bank allocate their development resources on the basis of such data. The paucity of accurate statistics is not merely a technical problem; it has a massive impact on the welfare of citizens in developing countries. Where do these statistics originate? How accurate are they? Poor Numbers is the first analysis of the production and use of African economic development statistics. Morten Jerven's research shows how the statistical capacities of sub-Saharan African economies have fallen into disarray. The numbers substantially misstate the actual state of affairs. As a result, scarce resources are misapplied. Development policy does not deliver the benefits expected. Policymakers' attempts to improve the lot of the citizenry are frustrated. Donors have no accurate sense of the impact of the aid they supply. Jerven's findings from sub-Saharan Africa have far-reaching implications for aid and development policy. As Jerven notes, the current catchphrase in the development community is "evidence-based policy," and scholars are applying increasingly sophisticated econometric methods-but no statistical techniques can substitute for partial and unreliable data.
A provocative ontological-cum-semantic position asserting that the right ontology is austere in its exclusion of numerous common-sense and scientific posits and that many statements employing such posits are nonetheless true. The authors of Austere Realism describe and defend a provocative ontological-cum-semantic position, asserting that the right ontology is minimal or austere, in that it excludes numerous common-sense posits, and that statements employing such posits are nonetheless true, when truth is understood to be semantic correctness under contextually operative semantic standards. Terence Horgan and Matjaz Potrc argue that austere realism emerges naturally from consideration of the deep problems within the naive common-sense approach to truth and ontology. They offer an account of truth that confronts these deep internal problems and is independently plausible: contextual semantics, which asserts that truth is semantically correct affirmability. Under contextual semantics, much ordinary and scientific thought and discourse is true because its truth is indirect correspondence to the world. After offering further arguments for austere realism and addressing objections to it, Horgan and Potrc consider various alternative austere ontologies. They advance a specific version they call “blobjectivism”—the view that the right ontology includes only one concrete particular, the entire cosmos (“the blobject”), which, although it has enormous local spatiotemporal variability, does not have any proper parts. The arguments in Austere Realism are powerfully made and concisely and lucidly set out. The authors' contentions and their methodological approach—products of a decade-long collaboration—will generate lively debate among scholars in metaphysics, ontology, and philosophy.
In the 1920s, when Laura Dillon felt like a man trapped in a woman's body, there were no words to describe her condition; transsexual had yet to enter common usage. And there was no known solution to being stuck between the sexes. In a desperate bid to feel comfortable in her own skin, she experimented with breakthrough technologies that ultimately transformed the human body and revolutionized medicine. Michael Dillon's incredible story, from upper-class orphan girl to Buddhist monk, reveals the struggles of early transsexuals and challenges conventional notions of what gender really means.