The book contains a wealth of detailed and fascinating case studies of New Public Management (NPM) in practice in the UK, exploring the enactment of NPM in its specific organizational contexts. A range of public services are covered including local government, education, social work and the police, with particular attention paid to the National Health Service. The editors introduce the case studies through an examination of the 'hydra-headed' nature of NPM, its variability between sectors and its contested character. This provides themes that are developed within the case studies, where, in varying organizational contexts, the meaning of NPM is negotiated and its impact on those working in the organization is explored. The book points to the complex, fluid and negotiated character of NPM, as well as its centrality in reconfiguring occupational identities and relations within public service organizations.

Rather than diminishing with time, the fascination with the Confederacy and its heroes seems to grow increasingly stronger. This volume features a wide selection of rarely seen photographs of Confederate heroes, such as Robert E. Lee, ""Stonewall"" Jackson, Jubal Early, Nathan Bedford Forest, A.P. Hill and Jeb Stuart, along with details of their military careers and personal lives that are little known to the average reader.Arranged chronologically and geographically, this book features descriptions of more than forty battles of the War Between the States, along with battle maps, which illustrate where the Confederates and the Union antagonists were located during these various fights. The first section discusses and provides images from 1860 to 1861, during the secession to Fort Sumter. The author then discusses the major battles that occurred in 1861 in northern Virginia and in the South and West, providing several photographs that take on much of the story telling. The sections for the years 1862 to 1865 are arranged similarly and each section also includes background about the Southern battle flags from various groups such as The Texas Rangers (8th Texas Calvary) and the Confederate Navy.

This insightful book draws on a range of contemporary and classic studies to explore the connection between the personal experience of work and the wider social structures in which it takes place. Identity at Work examines key social identities relevant to the workplace, such as those based on gender, sexual orientation, ethnicity and race, disability, age, occupation, class and organizational membership. Using research from a wide variety of countries and academic approaches, this book provides a readable and engaging introduction to the issues, exploring how people experience work, understand and present themselves at work, and relate to others. Providing an accessible investigation of work and identity, this text will be valuable to students looking at organizational behaviour, HRM, diversity management and the sociology of work.

This insightful book draws on a range of contemporary and classic studies to explore the connection between the personal experience of work and the wider social structures in which it takes place. Identity at Work examines key social identities relevant to the workplace, such as those based on gender, sexual orientation, ethnicity and race, disability, age, occupation, class and organizational membership. Using research from a wide variety of countries and academic approaches, this book provides a readable and engaging introduction to the issues, exploring how people experience work, understand and present themselves at work, and relate to others. Providing an accessible investigation of work and identity, this text will be valuable to students looking at organizational behaviour, HRM, diversity management and the sociology of work.

This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.

This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.

A collection of original essays by distinguished historians on the works of topographical writers who described and recorded the landscape of South-West England in the period c. 1540-1900. The development, subject matter and contribution to knowledge of a range of key authors is examined. For example, John Leland's classic descriptions of South-West England will be assessed and the works of local writers in the Tudor and Stuart era who followed an developed his approach to the description of people and places is examined. Amongst these, Richard Carew of Anthony produced perhaps the finest of any of the descriptions of an English region in his study of Cornwall, published in 1602. The authors follow the writings of Devon, Cornwall, Somerset and Dorset topographers who contributed to the genre over more than three centuries. The book also includes a gazetter of collections in Devon and Cornwall where copies of the works of local topographical writers can be found.