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Bauman and contemporary sociology: A critical analysis

This is the first single-authored critical engagement with the major works of Zygmunt Bauman. Where previous books on Bauman have been exegetical, here an unwavering light is shone on key themes in the sociologist’s work, exposing serious weaknesses in Bauman’s interpretations of the Holocaust, Western modernity, consumerism, globalisation and the nature of sociology. The book shows how Eurocentrism, the neglect of issues of gender and a lack of awareness of the racism faced by Europe’s non-white ethnic minorities seriously limit Bauman’s analyses of Western societies. At the same time, it points to Bauman’s repeated insistence on the need for sociologists to take a moral stance in favour of the world’s poor and downtrodden as being his most valuable legacy. The book will be of great interest to sociologists. Its readability will be valued by undergraduates and postgraduates and it will attract a readership well beyond the discipline.

Bauman and contemporary sociology: A critical analysis

This is the first single-authored critical engagement with the major works of Zygmunt Bauman. Where previous books on Bauman have been exegetical, here an unwavering light is shone on key themes in the sociologist’s work, exposing serious weaknesses in Bauman’s interpretations of the Holocaust, Western modernity, consumerism, globalisation and the nature of sociology. The book shows how Eurocentrism, the neglect of issues of gender and a lack of awareness of the racism faced by Europe’s non-white ethnic minorities seriously limit Bauman’s analyses of Western societies. At the same time, it points to Bauman’s repeated insistence on the need for sociologists to take a moral stance in favour of the world’s poor and downtrodden as being his most valuable legacy. The book will be of great interest to sociologists. Its readability will be valued by undergraduates and postgraduates and it will attract a readership well beyond the discipline.

Bauman's Challenge: Sociological Issues for the 21st Century

This unique and original collection by internationally renowned scholars uses critical engagements with Zygmunt Bauman's sociology to understand the challenges that face globalized human societies at the start of the 21st century. Includes a concluding chapter by Bauman.

Bausteine der Energiewende (RaumFragen: Stadt – Region – Landschaft)

Die Energiewende verändert in Deutschland mit dem Ausstieg aus der Kernkraft und dem Ausbau erneuerbarer Energien in weitreichender Weise bisherige Strukturen der Energieversorgung und wirkt sich dabei räumlich stark aus. Biomasse-, Windkraft- und Photovoltaikanlagen stellen einige der physisch sichtbaren Manifestationen dar. Hinzukommen neue Stromtrassen. Diese Entwicklungsprozesse laufen allerdings keineswegs konfliktfrei ab. Das Buch gibt Einblicke in unterschiedliche Facetten, unterschiedliche Bausteine der Energiewende und ordnet diese ein.Die HerausgeberDr. Dr. Olaf Kühne ist Professor für Stadt- und Regionalentwicklung an der Eberhard Karls Universität Tübingen.Dr. Florian Weber ist Akademischer Rat im Forschungsbereich Stadt- und Regionalentwicklung an der Eberhard Karls Universität Tübingen.

Bausteine syntaktischen Wissens: Ein Lehrbuch der generativen Grammatik

Chomskys "Rektions- und Bindungstheorie" ist die gemeinsame Sprache der generativ arbeitenden Syntaktiker unserer Tage. Die beiden Autoren legen hier eine umfassende Einführung in die Grundlagen und den neuesten Stand dieser Theorie vor. Das Buch wurde als verläßliches Lehr- und Nachschlagewerk konzipiert, es liefert ein geschlossenes Lehrgebäude, das in zahlreichen Lehrveranstaltungen erprobt und laufend verbessert wurde. Aufbau und Darstellung zeichnen sich durch Kohärenz und Verständlichkeit aus. Neben den klassischen Beispielsprachen (Englisch, Holländisch unddie romanischen Sprachen) spielt auch das Deutsche eine wichtigeRolle für die Anwendung der Theorie.

Bauxite Mining in Africa: Transnational Corporate Governance and Development (International Political Economy Series)

This book deals with the consequences of the inclusion of African states and communities in the global aluminium chain. The so-called “New Scramble for Africa” of the 2000s illustrated how seriously African living conditions are affected by continuous cycles of boom and bust, and how strongly the quality of life currently depends on the investment decisions and corporate social responsibility policies of transnational corporations. Taking the example of the global production network of bauxite and aluminium, the author focuses on the socio-political aspects of this dependency, which he achieves through the conducting of a series of interviews with various involved parties.

Bayern: Creating a Global Superclub

‘MASTERFUL’ Raphael HonigsteinThe story of superclub Bayern Munich by the critically acclaimed author of Tor!Bayern Munich is a team of extremes. They are the most passionately supported club in Germany and the most hated. There is no doubt that they are the most successful.Winners of twenty-four domestic titles since the late 1960s, they have stood at the pinnacle of European football for almost their entire existence. Through interviews with the key protagonists, Uli Hesse tells the story of this unique club. From early run-ins with the Nazis to being dubbed FC Hollywood for their egocentric stars in the 1990s up to the sensational undercover appointment of the best coach in the world, Pep Guardiola, Hesse opens the doors on Bavaria’s superpower and takes you inside Bayern Munich.

Bayes Factors for Forensic Decision Analyses with R (Springer Texts in Statistics)

Bayes Factors for Forensic Decision Analyses with R provides a self-contained introduction to computational Bayesian statistics using R. With its primary focus on Bayes factors supported by data sets, this book features an operational perspective, practical relevance, and applicability—keeping theoretical and philosophical justifications limited. It offers a balanced approach to three naturally interrelated topics:Probabilistic Inference - Relies on the core concept of Bayesian inferential statistics, to help practicing forensic scientists in the logical and balanced evaluation of the weight of evidence.Decision Making - Features how Bayes factors are interpreted in practical applications to help address questions of decision analysis involving the use of forensic science in the law.Operational Relevance - Combines inference and decision, backed up with practical examples and complete sample code in R, including sensitivity analyses and discussion on how to interpret results in context.Over the past decades, probabilistic methods have established a firm position as a reference approach for the management of uncertainty in virtually all areas of science, including forensic science, with Bayes' theorem providing the fundamental logical tenet for assessing how new information—scientific evidence—ought to be weighed. Central to this approach is the Bayes factor, which clarifies the evidential meaning of new information, by providing a measure of the change in the odds in favor of a proposition of interest, when going from the prior to the posterior distribution. Bayes factors should guide the scientist's thinking about the value of scientific evidence and form the basis of logical and balanced reporting practices, thus representing essential foundations for rational decision making under uncertainty.This book would be relevant to students, practitioners, and applied statisticians interested in inference and decision analyses in the critical field of forensic science. It could be used to support practical courses on Bayesian statistics and decision theory at both undergraduate and graduate levels, and will be of equal interest to forensic scientists and practitioners of Bayesian statistics for driving their evaluations and the use of R for their purposes.This book is Open Access.

Bayes-Statistik für Human- und Sozialwissenschaften (Springer-Lehrbuch)

Dieses Lehrbuch erklärt verständlich, welche Vorteile die Bayes-Statistik den Human- und Sozialwissenschaften bietet, warum sie der klassischen Statistik mitunter überlegen ist und wie man sie konkret anwendet. Es beginnt bei den erkenntnistheoretischen Grundlagen der Bayes-Statistik und erklärt speziell (aber nicht nur) für die typischen Fragen der Human- und Sozialwissenschaften, wie sich diese mit einfachen Formeln behandeln lassen. Wolfgang Tschirk vermittelt überzeugend, warum die Bayes-Statistik eine Krone der Wahrscheinlichkeitsrechnung ist: Weil sie auch spärliche Informationen zu einem Problem so kombiniert, dass die Schlüsse ein Maximum an Wahrscheinlichkeit gewinnen – ein Vorteil besonders in den Wissenschaften vom Menschen, die mit ihrem Forschungsobjekt nicht beliebig experimentieren können.

Bayesian Argumentation: The practical side of probability (Synthese Library #362)

Relevant to, and drawing from, a range of disciplines, the chapters in this collection show the diversity, and applicability, of research in Bayesian argumentation. Together, they form a challenge to philosophers versed in both the use and criticism of Bayesian models who have largely overlooked their potential in argumentation. Selected from contributions to a multidisciplinary workshop on the topic held in Sweden in 2010, the authors count linguists and social psychologists among their number, in addition to philosophers. They analyze material that includes real-life court cases, experimental research results, and the insights gained from computer models. The volume provides, for the first time, a formal measure of subjective argument strength and argument force, robust enough to allow advocates of opposing sides of an argument to agree on the relative strengths of their supporting reasoning. With papers from leading figures such as Michael Oaksford and Ulrike Hahn, the book comprises recent research conducted at the frontiers of Bayesian argumentation and provides a multitude of examples in which these formal tools can be applied to informal argument. It signals new and impending developments in philosophy, which has seen Bayesian models deployed in formal epistemology and philosophy of science, but has yet to explore the full potential of Bayesian models as a framework in argumentation. In doing so, this revealing anthology looks destined to become a standard teaching text in years to come.​

Bayesian Evaluation of Informative Hypotheses (Statistics for Social and Behavioral Sciences #Vol. 18)

This book provides an overview of the developments in the area of Bayesian evaluation of informative hypotheses that took place since the publication of the ?rst paper on this topic in 2001 [Hoijtink, H. Con?rmatory latent class analysis, model selection using Bayes factors and (pseudo) likelihood ratio statistics. Multivariate Behavioral Research, 36, 563–588]. The current state of a?airs was presented and discussed by the authors of this book during a workshop in Utrecht in June 2007. Here we would like to thank all authors for their participation, ideas, and contributions. We would also like to thank Sophie van der Zee for her editorial e?orts during the construction of this book. Another word of thanks is due to John Kimmel of Springer for his con?dence in the editors and authors. Finally, we would like to thank the Netherlands Organization for Scienti?c Research (NWO) whose VICI grant (453-05-002) awarded to the ?rst author enabled the organization of the workshop, the writing of this book, and continuation of the research with respect to Bayesian evaluation of informative hypotheses.

Bayesian Inference in the Social Sciences

Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include: Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performance State-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book’s supplemental website Interdisciplinary coverage from well-known international scholars and practitioners Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences.

Bayesian Inference in the Social Sciences

Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include: Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performance State-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book’s supplemental website Interdisciplinary coverage from well-known international scholars and practitioners Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences.

Bayesian Item Response Modeling: Theory and Applications (Statistics for Social and Behavioral Sciences)

The modeling of item response data is governed by item response theory, also referred to as modern test theory. The eld of inquiry of item response theory has become very large and shows the enormous progress that has been made. The mainstream literature is focused on frequentist statistical methods for - timating model parameters and evaluating model t. However, the Bayesian methodology has shown great potential, particularly for making further - provements in the statistical modeling process. The Bayesian approach has two important features that make it attractive for modeling item response data. First, it enables the possibility of incorpor- ing nondata information beyond the observed responses into the analysis. The Bayesian methodology is also very clear about how additional information can be used. Second, the Bayesian approach comes with powerful simulation-based estimation methods. These methods make it possible to handle all kinds of priors and data-generating models. One of my motives for writing this book is to give an introduction to the Bayesian methodology for modeling and analyzing item response data. A Bayesian counterpart is presented to the many popular item response theory books (e.g., Baker and Kim 2004; De Boeck and Wilson, 2004; Hambleton and Swaminathan, 1985; van der Linden and Hambleton, 1997) that are mainly or completely focused on frequentist methods. The usefulness of the Bayesian methodology is illustrated by discussing and applying a range of Bayesian item response models.

Bayesian Methods in Statistics: From Concepts to Practice

This book walks you through learning probability and statistics from a Bayesian point of view. From an introduction to probability theory through to frameworks for doing rigorous calculations of probability, it discusses Bayes’ Theorem before illustrating how to use it in a variety of different situations with data addressing social and psychological issues. The book also: Equips you with coding skills in the statistical modelling language Stan and programming language R. Discusses how Bayesian approaches to statistics compare to classical approaches. Introduces Markov Chain Monte Carlo methods for doing Bayesian statistics through computer simulations, so you understand how Bayesian solutions are implemented. Features include an introduction to each chapter and a chapter summary to help you check your learning. All the examples and data used in the book are also available in the online resources so you can practice at your own pace. For readers with some understanding of basic mathematical functions and notation, this book will get you up and running so you can do Bayesian statistics with confidence.

Bayesian Methods in Statistics: From Concepts to Practice

This book walks you through learning probability and statistics from a Bayesian point of view. From an introduction to probability theory through to frameworks for doing rigorous calculations of probability, it discusses Bayes’ Theorem before illustrating how to use it in a variety of different situations with data addressing social and psychological issues. The book also: Equips you with coding skills in the statistical modelling language Stan and programming language R. Discusses how Bayesian approaches to statistics compare to classical approaches. Introduces Markov Chain Monte Carlo methods for doing Bayesian statistics through computer simulations, so you understand how Bayesian solutions are implemented. Features include an introduction to each chapter and a chapter summary to help you check your learning. All the examples and data used in the book are also available in the online resources so you can practice at your own pace. For readers with some understanding of basic mathematical functions and notation, this book will get you up and running so you can do Bayesian statistics with confidence.

Bayesian Methods in Statistics: From Concepts to Practice

This book walks you through learning probability and statistics from a Bayesian point of view. From an introduction to probability theory through to frameworks for doing rigorous calculations of probability, it discusses Bayes’ Theorem before illustrating how to use it in a variety of different situations with data addressing social and psychological issues. The book also: Equips you with coding skills in the statistical modelling language Stan and programming language R. Discusses how Bayesian approaches to statistics compare to classical approaches. Introduces Markov Chain Monte Carlo methods for doing Bayesian statistics through computer simulations, so you understand how Bayesian solutions are implemented. Features include an introduction to each chapter and a chapter summary to help you check your learning. All the examples and data used in the book are also available in the online resources so you can practice at your own pace. For readers with some understanding of basic mathematical functions and notation, this book will get you up and running so you can do Bayesian statistics with confidence.

Bayesian Networks in Educational Assessment (Statistics for Social and Behavioral Sciences)

Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments.Part I develops Bayes nets’ foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A unique feature is the volume’s grounding in Evidence-Centered Design (ECD) framework for assessment design. This “design forward” approach enables designers to take full advantage of Bayes nets’ modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD, situates Bayes nets as an integral component of a principled design process, and illustrates the ideas with an in-depth look at the BioMass project: An interactive, standards-based, web-delivered demonstration assessment of science inquiry in genetics.This book is both a resource for professionals interested in assessment and advanced students. Its clear exposition, worked-through numerical examples, and demonstrations from real and didactic applications provide invaluable illustrations of how to use Bayes nets in educational assessment. Exercises follow each chapter, and the online companion site provides a glossary, data sets and problem setups, and links to computational resources.

Bayesian Statistical Modeling with Stan, R, and Python

This book provides a highly practical introduction to Bayesian statistical modeling with Stan, which has become the most popular probabilistic programming language.The book is divided into four parts. The first part reviews the theoretical background of modeling and Bayesian inference and presents a modeling workflow that makes modeling more engineering than art. The second part discusses the use of Stan, CmdStanR, and CmdStanPy from the very beginning to basic regression analyses. The third part then introduces a number of probability distributions, nonlinear models, and hierarchical (multilevel) models, which are essential to mastering statistical modeling. It also describes a wide range of frequently used modeling techniques, such as censoring, outliers, missing data, speed-up, and parameter constraints, and discusses how to lead convergence of MCMC. Lastly, the fourth part examines advanced topics for real-world data: longitudinal data analysis, state space models, spatial data analysis, Gaussian processes, Bayesian optimization, dimensionality reduction, model selection, and information criteria, demonstrating that Stan can solve any one of these problems in as little as 30 lines.Using numerous easy-to-understand examples, the book explains key concepts, which continue to be useful when using future versions of Stan and when using other statistical modeling tools. The examples do not require domain knowledge and can be generalized to many fields. The book presents full explanations of code and math formulas, enabling readers to extend models for their own problems. All the code and data are on GitHub.

Bayesian Statistics in Action: BAYSM 2016, Florence, Italy, June 19-21 (Springer Proceedings in Mathematics & Statistics #194)

This book is a selection of peer-reviewed contributions presented at the third Bayesian Young Statisticians Meeting, BAYSM 2016, Florence, Italy, June 19-21. The meeting provided a unique opportunity for young researchers, M.S. students, Ph.D. students, and postdocs dealing with Bayesian statistics to connect with the Bayesian community at large, to exchange ideas, and to network with others working in the same field. The contributions develop and apply Bayesian methods in a variety of fields, ranging from the traditional (e.g., biostatistics and reliability) to the most innovative ones (e.g., big data and networks).

The BBC: Public Institution and Private World (pdf) (Edinburgh Studies in Sociology)

BBC Sport in Black and White (Palgrave Studies in the History of the Media)

This book provides the first detailed account of the formative decades of BBC televised sport when it launched its flagship programmes Sportsview, Grandstand and Match of the Day. Based on extensive archival research in the BBC’s written archives and interviews with leading producers, editors and commentators of the period, it provides a ‘behind-the-scenes’ narrative history of this major institution of British cultural life. In 2016 the BBC celebrated the fiftieth anniversary of its television coverage of England’s World Cup victory. Their coverage produced one of the most oft-played moments in the history of television, Kenneth Wolstenholme’s famous line: ‘Some people are on the pitch, they think it’s all over … it is now!’ as Geoff Hurst scored England’s fourth goal, securing England’s 4-2 victory. It was a landmark in English football as well as a watershed in the BBC’s highly professionalised approach to televised sport. How the BBC reached this peak of television expertise, and who was behind their success in developing the techniques of televised sport, is the focus of this book.

BDSM in American Science Fiction and Fantasy

A history of the love affair between BDSM (Bondage/Discipline, Dominance/Submission, Sadism/Masochism) and science fiction and fantasy. Lewis Call explores representations of BDSM in the 1940s Wonder Woman comics, the pioneering prose of Samuel Delany and James Tiptree, and the television shows Battlestar Galactica, Buffy, Angel and Dollhouse.

Be Brave: A Child's Guide to Overcoming Shyness

An interactive workbook for parents and children from the author of the best-selling titles Don’t Worry, Be Happy: A Child’s Guide to Overcoming Anxiety and You’re a Star: A Child’s Guide to Self-EsteemDoes your child appear nervous and isolated in social settings?Perhaps they find it difficult to approach other children or make friends?Do they seem to avoid engaging in hobbies and activities?These could all be signs that your child is struggling with shyness.This practical guide combines cognitive behavioural therapy and mindfulness methods with simple activities to help your child overcome shyness. It’s aimed at children aged 7–11 because a lot happens in these years that can impact a child’s confidence, not just now but for years to come.Your child will be guided, with the help of Jem – a friendly and supportive character they can identify with – through fun and engaging activities which are interspersed with useful tips, inspirational statements and practical information for parents.

Be Bulletproof: How to achieve success in tough times at work

This is the essential guide for anyone looking to get ahead in the warzone that is often the workplace.However good you are, there are always times you come under fire at work. But how do you turn a crisis into an opportunity, and make yourself bulletproof?In Be Bulletproof, business trainers James and Simon Brooke reveal the top practical solutions for strengthening your resilience – so you can bounce back from every setback, rejection or criticism. You’ll learn to be confident, positive and self-assured in the face of any office adversity.Arm yourself against workplace hazards like:- Harsh criticism and hostile colleagues- Company politics and bad bosses - Rejection and failure - Redundancy or losing your job - And – dare we say it? – your own mistakes