Sharon Shoesmith was Director of Children's Services for Haringey in 2007 at the time of the death of Peter Connelly, also known as 'Baby P'. In Learning from Baby P, she carries out a dispassionate analysis of the events which followed Peter Connelly's death, documenting the responses of the media, politicians and the public. She explores the psychological and emotional responses we share when faced with such horrifying cases of familial child homicide, and how a climate of fear and blame which follows such tragedies can lead to negative consequences for other children at risk of harm, and for the social workers striving to protect them. Learning from Baby P is a thought-provoking book which aims to deepen understanding and shed light on the difficult relationship between politics, the media and child protection.

In England in 2007 Peter Connelly, a 17 month old little boy - known initially in the media reporting as 'Baby P' - died following terrible neglect and abuse. Fifteen months later, his mother, her boyfriend and the boyfriend's brother were sent to prison. But media attention turned on those who worked to protect children, especially the social workers and their managers, who became the focus of the reporting and of the blame. This book tells what happened to 'Baby P', how the story was told and became focused on the social workers, its threatening consequences for those who work to protect children, and its considerable impact on the child protection system in England. This is the first book to draw together all evidence available on this high profile case and will make a unique and crucial contribution to the topic. It will make essential reading for everyone who is concerned about child protection and the care of children and about the media's impact. This revised edition contains a new Afterword bringing the story up to date.

Why and how to apply Systems Thinking to the design, structure and day-to-day running of your organisation.

How do children learn that the word "dog" refers not to all four-legged animals, and not just to Ralph, but to all members of a particular species? How do they learn the meanings of verbs like "think," adjectives like "good," and words for abstract entities such as "mortgage" and "story"? The acquisition of word meaning is one of the fundamental issues in the study of mind. According to Paul Bloom, children learn words through sophisticated cognitive abilities that exist for other purposes. These include the ability to infer others' intentions, the ability to acquire concepts, an appreciation of syntactic structure, and certain general learning and memory abilities. Although other researchers have associated word learning with some of these capacities, Bloom is the first to show how a complete explanation requires all of them. The acquisition of even simple nouns requires rich conceptual, social, and linguistic capacities interacting in complex ways. This book requires no background in psychology or linguistics and is written in a clear, engaging style. Topics include the effects of language on spatial reasoning, the origin of essentialist beliefs, and the young child's understanding of representational art. The book should appeal to general readers interested in language and cognition as well as to researchers in the field.

Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos Table of Contents PART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce

Case studies of near-death experiences in children reveal the patients' ability to communicate with deceased relatives and friends, as well as their experiences while dead

Children are already learning at birth, and they develop and learn at a rapid pace in their early years. This provides a critical foundation for lifelong progress, and the adults who provide for the care and the education of young children bear a great responsibility for their health, development, and learning. Despite the fact that they share the same objective - to nurture young children and secure their future success - the various practitioners who contribute to the care and the education of children from birth through age 8 are not acknowledged as a workforce unified by the common knowledge and competencies needed to do their jobs well. Transforming the Workforce for Children Birth Through Age 8 explores the science of child development, particularly looking at implications for the professionals who work with children. This report examines the current capacities and practices of the workforce, the settings in which they work, the policies and infrastructure that set qualifications and provide professional learning, and the government agencies and other funders who support and oversee these systems. This book then makes recommendations to improve the quality of professional practice and the practice environment for care and education professionals. These detailed recommendations create a blueprint for action that builds on a unifying foundation of child development and early learning, shared knowledge and competencies for care and education professionals, and principles for effective professional learning. Young children thrive and learn best when they have secure, positive relationships with adults who are knowledgeable about how to support their development and learning and are responsive to their individual progress. Transforming the Workforce for Children Birth Through Age 8 offers guidance on system changes to improve the quality of professional practice, specific actions to improve professional learning systems and workforce development, and research to continue to build the knowledge base in ways that will directly advance and inform future actions. The recommendations of this book provide an opportunity to improve the quality of the care and the education that children receive, and ultimately improve outcomes for children.