Joyce Dixson killed a man. As she stood in the courtroom and listened to the Judge issue the verdict, bewildered, she thought: "How could my life have come to this?" The next thing she thought was: "What is going to happen to my children?"
Her children were just two children in millions, who are still living with parents in prison. There wasn't much information available on this population when she went to prison. However, Levels of Response to Traumatic Events is a tool that will equip family, lay people and professionals alike in effectively helping and working with children of incarcerated parents.
LORTE is: A tool that describes the journey the children will take; and the stops they will make along the way A tool that tracks, defines and explains adverse behavior A tool that allows the youth, caregiver and professional to identify where the youth is in the cycle A way to simplify the task of creating effective treatment goals LORTE is a tool for resiliency. We can give our children what they need to bounce back. Levels of Response to Traumatic Events is a tool that makes resiliency more than possible.
This collection focuses on the multi-layered links between international events and identity discourses. With a unique line-up of international scholars, this book offers a diverse range of exciting case studies, including sports competitions, music festivals, exhibitions, fashion shows and royal celebrations.
The book offers a detailed guide to temporal ordering, exploring open problems in the field and providing solutions and extensive analysis. It addresses the challenge of automatically ordering events and times in text. Aided by TimeML, it also describes and presents concepts relating to time in easy-to-compute terms. Working out the order that events and times happen has proven difficult for computers, since the language used to discuss time can be vague and complex. Mapping out these concepts for a computational system, which does not have its own inherent idea of time, is, unsurprisingly, tough. Solving this problem enables powerful systems that can plan, reason about events, and construct stories of their own accord, as well as understand the complex narratives that humans express and comprehend so naturally.
This book presents a theory and data-driven analysis of temporal ordering, leading to the identification of exactly what is difficult about the task. It then proposes and evaluates machine-learning solutions for the major difficulties.
It is a valuable resource for those working in machine learning for natural language processing as well as anyone studying time in language, or involved in annotating the structure of time in documents.
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