"An excellent introduction to the essential problem of our republic. With a wake-up call like this one, we still have a chance." -Timothy Snyder, author of On Tyranny Ghosting the News tells the most troubling media story of our time: How democracy suffers when local news dies. From 2004 to 2015, 1,800 print newspaper outlets closed in the US. One in five news organizations in Canada has closed since 2008. One in three Brazilians lives in news deserts. The absence of accountability journalism has created an atmosphere in which indicted politicians were elected, school superintendents were mismanaging districts, and police chiefs were getting mysterious payouts. This is not the much-discussed fake-news problem-it's the separate problem of a critical shortage of real news. America's premier media critic, Margaret Sullivan, charts the contours of the damage, and surveys a range of new efforts to keep local news alive-from non-profit digital sites to an effort modeled on the Peace Corps. No nostalgic paean to the roar of rumbling presses, Ghosting the News instead sounds a loud alarm, alerting citizens to a growing crisis in local news that has already done serious damage.

This book describes statistical techniques for the design and evaluation of research studies on medical diagnostic tests, screening tests, biomarkers and new technologies for classification and prediction in medicine.

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The authors deliver concise annotations of 85 picture books; notations about the illustrative elements found in each book are followed by novel, theme, and curriculum connections. A chart is included, showing how each of the tides connects to different areas of the curriculum. Practical language arts, math, science, social studies, art, and music activities are suggested in ways to encourage a deeper understanding of the books.

This book describes statistical concepts and techniques for evaluating medical diagnostic tests and biomarkers for detecting disease. More generally, the techniques pertain to the statistical classification problem for predicting a dichotomous outcome. Measures for quantifying test accuracy are described including sensitivity, specificity, predictive values, diagnostic likelihood ratios and the Receiver Operating Characteristic Curve that is commonly used for continuous and ordinal valued tests. Statistical procedures are presented for estimating and comparing them. Regression frameworks for assessing factors that influence test accuracy and for comparing tests while adjusting for such factors are presented. This book presents many worked examples of real data and should be of interest to practicing statisticians or quantitative researchers involved in the development of tests for classification or prediction in medicine.