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What is knowledge? How do we achieve it? Why is it worth striving for? Is knowledge possible at all? Starting from these traditional questions about the nature, source, and value of knowledge, as well as its possibility and limits, the author investigates the most important positions in current epistemology. She then defends her own approach which meets the requirements of epistemology and philosophy of language with regard to a philosophically satisfying conception of knowledge.

What is knowledge? How do we achieve it? Why is it worth striving for? Is knowledge possible at all? Starting from these traditional questions about the nature, source, and value of knowledge, as well as its possibility and limits, the author investigates the most important positions in current epistemology. She then defends her own approach which meets the requirements of epistemology and philosophy of language with regard to a philosophically satisfying conception of knowledge.

Decades of research in the cognitive and learning sciences have led to a growing recognition of the incredibly multi-faceted nature of human knowing and learning. Up to now, this multifaceted nature has been visible mostly in distinct and often competing communities of researchers. From a purely scientific perspective, "siloed" science―where different traditions refuse to speak with one another, or merely ignore one another―is unacceptable. This ambitious volume attempts to kick-start a serious, new line of work that merges, or properly articulates, different traditions with their divergent historical, theoretical, and methodological commitments that, nonetheless, both focus on the highly detailed analysis of processes of knowing and learning as they unfold in interactional contexts in real time. Knowledge and Interaction puts two traditions in dialogue with one another: Knowledge Analysis (KA), which draws on intellectual roots in developmental psychology and cognitive modeling and focuses on the nature and form of individual knowledge systems, and Interaction Analysis (IA), which has been prominent in approaches that seek to understand and explain learning as a sequence of real-time moves by individuals as they interact with interlocutors, learning environments, and the world around them. The volume’s four-part organization opens up space for both substantive contributions on areas of conceptual and empirical work as well as opportunities for reflection, integration, and coordination.

Decades of research in the cognitive and learning sciences have led to a growing recognition of the incredibly multi-faceted nature of human knowing and learning. Up to now, this multifaceted nature has been visible mostly in distinct and often competing communities of researchers. From a purely scientific perspective, "siloed" science―where different traditions refuse to speak with one another, or merely ignore one another―is unacceptable. This ambitious volume attempts to kick-start a serious, new line of work that merges, or properly articulates, different traditions with their divergent historical, theoretical, and methodological commitments that, nonetheless, both focus on the highly detailed analysis of processes of knowing and learning as they unfold in interactional contexts in real time. Knowledge and Interaction puts two traditions in dialogue with one another: Knowledge Analysis (KA), which draws on intellectual roots in developmental psychology and cognitive modeling and focuses on the nature and form of individual knowledge systems, and Interaction Analysis (IA), which has been prominent in approaches that seek to understand and explain learning as a sequence of real-time moves by individuals as they interact with interlocutors, learning environments, and the world around them. The volume’s four-part organization opens up space for both substantive contributions on areas of conceptual and empirical work as well as opportunities for reflection, integration, and coordination.

Since the 1990s, the knowledge, culture, and entertainment industries have found themselves experimenting, not altogether voluntarily, with communicating complex information across multiple media platforms. Against a backdrop of competing national priorities, changing technologies, globalization, and academic capitalism, these industries have sought to reach increasingly differentiated local audiences, even as distributed production practices have made the lack of authorial control increasingly obvious. As Katie King describes in Networked Reenactments, science-styled television—such as the Secrets of Lost Empires series shown on the PBS program Nova—demonstrates how new technical and collaborative skills are honed by television producers, curators, hobbyists, fans, and even scholars. Examining how transmedia storytelling is produced across platforms such as television and the web, she analyzes what this all means for the humanities. What sort of knowledge projects take up these skills, attending to grain of detail, evoking affective intensities, and zooming in and out, representing multiple scales, as well as many different perspectives? And what might this mean for feminist transdisciplinary work, or something sometimes called the posthumanities?

Cover is black boards with copper lettering on spine. DJ in a mylar cover. Pages are clean and tight; this is a NEW book. Illustrated with photographs, mostly color, of the experts. Sections are: Dressage Horse; Rider; Competing; Tips on Riding Gaits, Paces, and Movements; Source Guide. A veritable Who's Who of top ranked international riders, trainers and researchers. ; Photographs; 8 3/4 x 9 3/4; 180 pages

Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica® notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.

The strategic-choice approach has a long pedigree in international relations. In an area often rent by competing methodologies, editors David A. Lake and Robert Powell take the best of accepted and contested knowledge among many theories. With the contributors to this volume, they offer a unifying perspective, which begins with a simple insight: students of international relations want to explain the choices actors make--whether these actors be states, parties, ethnic groups, companies, leaders, or individuals. This synthesis offers three new benefits: first, the strategic interaction of actors is the unit of analysis, rather than particular states or policies; second, these interactions are now usefully organized into analytic schemes, on which conceptual experiments may be based; and third, a set of methodological "bets" is then made about the most productive ways to analyze the interactions. Together, these elements allow the pragmatic application of theories that may apply to a myriad of particular cases, such as individuals protesting environmental degradation, governments seeking to control nuclear weapons, or the United Nations attempting to mobilize member states for international peacekeeping. Besides the editors, the six contributors to this book, all distinguished scholars of international relations, are Jeffry A. Frieden, James D. Morrow, Ronald Rogowski, Peter Gourevitch, Miles Kahler, and Arthur A. Stein. Their work is an invaluable introduction for scholars and students of international relations, economists, and government decision-makers.

This text aims to expose students to the history of Christian thought, which is a central part of the discipline of theology. The rationale behind it is that students should not complete an introductory course without having been exposed to a wide variety of competing approaches and having gained some broad knowledge of the tradition, rather than studying methodological concerns only. The extracts are accompanied by commentary and annotation. They are divided into themed chapters, and ordered chronologically within chapters. Figures from different periods of history are included within each chapter, in order to represent the breadth of theological traditions, while texts are also included which represent some form of ongoing debate, in order to meet pedagogical needs. Commentary on each text outlines the historical situation of the author and the literary context of the extract, while each chapter offers an introductory essay highlighting the central debates and concerns in that area and indicating how the texts illustrate and illuminate these.

Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica® notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.

The goal of the present volume is to discuss the notion of a 'conceptual framework' or 'conceptual scheme', which has been dominating much work in the analysis and justification of knowledge in recent years. More specifi­ cally, this volume is designed to clarify the contrast between two competing approaches in the area of problems indicated by this notion: On the one hand, we have the conviction, underlying much present-day work in the philosophy of science, that the best we can hope for in the justifi­ cation of empirical knowledge is to reconstruct the conceptual means actually employed by science, and to develop suitable models for analyzing conceptual change involved in the progress of science. This view involves the assumption that we should stop taking foundational questions of epistemology seriously and discard once and for all the quest for uncontrovertible truth. The result­ ing program of justifying epistemic claims by subsequently describing patterns of inferentially connected concepts as they are at work in actual science is closely connected with the idea of naturalizing epistemology, with concep­ tual relativism, and with a pragmatic interpretation of knowledge. On the other hand, recent epistemology tends to claim that no subsequent reconstruction of actually employed conceptual frameworks is sufficient for providing epistemic justification for our beliefs about the world. This second claim tries to resist the naturalistic and pragmatic approach to epistemology and insists on taking the epistemological sceptic seriously.

Epistemology is the philosophical study of knowledge. Epistemologists seek to understand knowledge’s nature and availability. What is knowledge? There are competing theories. Can we really have knowledge? Challenges abound. In this lively book, Stephen Hetherington introduces us to epistemological theorizing. He builds a theory and tests it, refines it, and challenges it again. He explores such topics as evidence, truth and belief, different kinds of knowledge, and knowledge’s value, as well as sceptical views concerning knowledge of the physical world, one’s own mind and memory, and rational limits for observation and reason. This epistemological theorizing is then applied to some of life’s most pressing issues, such as how to live and how to understand ourselves and others. What is Epistemology? is a practical and student-friendly guide to one of the most dynamic areas of philosophy. It will be the go-to introductory epistemology text.

At the upper levels of performance in situations where competing individuals and teams have all the technical skill and knowledge required to be successful, psychological factors become important determinants of outcome. The ability to control emotions, to communicate effectively and to perform under pressure is what separates winners from losers.

Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica® notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.

At the upper levels of performance in situations where competing individuals and teams have all the technical skill and knowledge required to be successful, psychological factors become important determinants of outcome. The ability to control emotions, to communicate effectively and to perform under pressure is what separates winners from losers.

Cameron Strang takes American scientific thought and discoveries away from the learned societies, museums, and teaching halls of the Northeast and puts the production of knowledge about the natural world in the context of competing empires and an expanding republic in the Gulf South. People often dismissed by starched northeasterners as nonintellectuals--Indian sages, African slaves, Spanish officials, Irishmen on the make, clearers of land and drivers of men--were also scientific observers, gatherers, organizers, and reporters. Skulls and stems, birds and bugs, rocks and maps, tall tales and fertile hypotheses came from them. They collected, described, and sent the objects that scientists gazed on and interpreted in polite Philadelphia. They made knowledge.Frontiers of Science offers a new framework for approaching American intellectual history, one that transcends political and cultural boundaries and reveals persistence across the colonial and national eras. The pursuit of knowledge in the United States did not cohere around democratic politics or the influence of liberty. It was, as in other empires, divided by multiple loyalties and identities, organized through contested hierarchies of ethnicity and place, and reliant on violence. By discovering the lost intellectual history of one region, Strang shows us how to recover a continent for science.

Instructor Resources: Instructor Manual and test bank.Consolidations and mergers have dramatically changed the face of the medical practice. From governance issues to information technology, today’s practice managers face a host of new complexities and competing priorities that demand more robust skills and knowledge than ever before.Fundamentals of Medical Practice Management uniquely addresses the educational needs of the modern practice manager. The author, who studied under quality guru W. Edwards Deming, combines a focus on quality and excellence with an important thesis: that working together and putting people first is the best path to success in healthcare. The author argues that win–lose games and quick-fix solutions have begun to deliver diminished returns in healthcare—not only economically but emotionally and societally. Although the book delves into many practical topics, its aim is to shape the reader’s mind-set for a new era of people-focused practice management.Fundamentals of Medical Practice Management covers these areas and more:· Information technology and management· Regulatory issues, law, and practice management· Strategic planning, project management, and marketing· Third-party payers and revenue cycle· Human resources management· Quality and emergency management This book will appeal to a wide range of learners, including those in organizational programs, undergraduate and master of health administration programs, and practice management certificate programs. Its insights will not only prepare those new to the field but also help current practice managers to retool and refocus.

Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica® notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.

Instructor Resources: Instructor Manual and test bank.Consolidations and mergers have dramatically changed the face of the medical practice. From governance issues to information technology, today’s practice managers face a host of new complexities and competing priorities that demand more robust skills and knowledge than ever before.Fundamentals of Medical Practice Management uniquely addresses the educational needs of the modern practice manager. The author, who studied under quality guru W. Edwards Deming, combines a focus on quality and excellence with an important thesis: that working together and putting people first is the best path to success in healthcare. The author argues that win–lose games and quick-fix solutions have begun to deliver diminished returns in healthcare—not only economically but emotionally and societally. Although the book delves into many practical topics, its aim is to shape the reader’s mind-set for a new era of people-focused practice management.Fundamentals of Medical Practice Management covers these areas and more:· Information technology and management· Regulatory issues, law, and practice management· Strategic planning, project management, and marketing· Third-party payers and revenue cycle· Human resources management· Quality and emergency management This book will appeal to a wide range of learners, including those in organizational programs, undergraduate and master of health administration programs, and practice management certificate programs. Its insights will not only prepare those new to the field but also help current practice managers to retool and refocus.

In On Religious Diversity Robert McKim distinguishes and examines a number of possible responses to the knowledge of diverse religious traditions that is available to all of us today. There is no escaping the fact that the presence of competing traditions now confronts each of the traditions in a new and forceful way. And there is widespread if inchoate recognition of genuine religious sensibilities and genuine religious seriousness in others. How might, and how should, an awareness of other traditions affect a member of a particular religious tradition? What attitudes should be taken to the beliefs and salvific prospects of members of other traditions? McKim examines several proposed answers to these questions, offering the deepest analysis to date of such options as exclusivism and inclusivism. He argues that what look like well-defined and discrete positions dissolve somewhat under scrutiny, revealing significantly different possibilities. McKim suggests where best to look for the most plausible answers and makes a case for the attractiveness of inclusivistic options. He pays particular attention to the religiously ambiguous nature of our circumstances and to the implications of this ambiguity.