LEADER 03415nam 2200565 450 001 9910793147503321 005 20220929160525.0 010 $a1-5017-2830-X 024 7 $a10.7591/9781501728303 035 $a(CKB)4100000006673424 035 $a(OCoLC)1132222945 035 $a(MdBmJHUP)muse71336 035 $a(DE-B1597)515543 035 $a(OCoLC)1100447458 035 $a(DE-B1597)9781501728303 035 $a(MiAaPQ)EBC6990464 035 $a(Au-PeEL)EBL6990464 035 $a(EXLCZ)994100000006673424 100 $a20220929d2006 uy 0 101 0 $aeng 135 $aur|||||||nn|n 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFront page girls $ewomen journalists in American culture and fiction, 1880-1930 /$fJean Marie Lutes 210 1$aIthaca, N.Y. :$cCornell University Press,$d[2006] 210 4$dİ2006 215 $a1 online resource (xi, 226 p. :)$cill. ; 311 $a0-8014-7412-4 311 $a0-8014-4235-4 320 $aIncludes bibliographical references and index. 327 $aInto the madhouse with girl stunt reporters -- The African American newswoman as national icon -- The original sob sisters : writers on trial -- A reporter-heroine's evolution -- From news to novels -- Epilogue : girl reporters on film. 330 $aThe first study of the role of the newspaperwoman in American literary culture at the turn of the twentieth century, this book recaptures the imaginative exchange between real-life reporters like Nellie Bly and Ida B. Wells and fictional characters like Henrietta Stackpole, the lady-correspondent in Henry James's Portrait of a Lady. It chronicles the exploits of a neglected group of American women writers and uncovers an alternative reporter-novelist tradition that runs counter to the more familiar story of gritty realism generated in male-dominated newsrooms. Taking up actual newspaper accounts written by women, fictional portrayals of female journalists, and the work of reporters-turned-novelists such as Willa Cather and Djuna Barnes, Jean Marie Lutes finds in women's journalism a rich and complex source for modern American fiction. Female journalists, cast as both standard-bearers and scapegoats of an emergent mass culture, created fictions of themselves that far outlasted the fleeting news value of the stories they covered.Front-Page Girls revives the spectacular stories of now-forgotten newspaperwomen who were not afraid of becoming the news themselves-the defiant few who wrote for the city desks of mainstream newspapers and resisted the growing demand to fill women's columns with fashion news and household hints. It also examines, for the first time, how women's journalism shaped the path from news to novels for women writers. 606 $aWomen journalists$zUnited States 606 $aWomen journalists in literature 606 $aJournalism and literature 606 $aJournalism$xSocial aspects$zUnited States 615 0$aWomen journalists 615 0$aWomen journalists in literature. 615 0$aJournalism and literature. 615 0$aJournalism$xSocial aspects 676 $a070.4082 700 $aLutes$b Jean Marie$f1967-$01564461 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910793147503321 996 $aFront page girls$93833528 997 $aUNINA LEADER 02655nam 2200613 450 001 9910830499003321 005 20221128133513.0 010 $a1-281-31078-6 010 $a9786611310783 010 $a0-470-70126-9 010 $a0-470-77572-6 010 $a0-470-77686-2 035 $a(CKB)1000000000405257 035 $a(EBL)351294 035 $a(OCoLC)476171554 035 $a(SSID)ssj0000204629 035 $a(PQKBManifestationID)11168585 035 $a(PQKBTitleCode)TC0000204629 035 $a(PQKBWorkID)10189591 035 $a(PQKB)11329031 035 $a(MiAaPQ)EBC351294 035 $a(MiAaPQ)EBC6976385 035 $a(Au-PeEL)EBL6976385 035 $a(EXLCZ)991000000000405257 100 $a20221128d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aModernist literature $echallenging fictions /$fVicki Mahaffey 210 1$aMalden, Massachusetts :$cBlackwell Pub.,$d[2007] 210 4$dİ2007 215 $a1 online resource (266 p.) 300 $aDescription based upon print version of record. 311 $a0-631-21306-6 320 $aIncludes bibliographical references and index. 327 $aModernist Literature: Challenging Fictions; Contents; Preface; Acknowledgments; Part I: Introduction; 1 Why Read Challenging Literature?; Part II: Readings; 2 Partnering: Holmes and Watson, Author and Reader, Lover and Loved, Man and Wife; 3 Window Painting: The Art of Blocking Understanding; 4 Watchman, What of the Night?; Conclusion; Notes; Bibliography; Index 330 $aThis inclusive guide to Modernist literature considers the 'high' Modernist writers such as Eliot, Joyce, Pound and Yeats alongside women writers and writers of the Harlem Renaissance.Challenges the idea that Modernism was conservative and reactionary. Relates the modernist impulse to broader cultural and historical crises and movements. Covers a wide range of authors up to the outbreak of World War II, among them Oscar Wilde, Joseph Conrad, Henry James, Langston Hughes, Samuel Beckett, HD, Virginia Woolf, Djuna Barnes, and Jean Rhys. Includes coverage o 606 $aModernism (Literature) 606 $aReader-response criticism 606 $aAuthors and readers 615 0$aModernism (Literature) 615 0$aReader-response criticism. 615 0$aAuthors and readers. 676 $a823.91209113 700 $aMahaffey$b Vicki$0533590 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830499003321 996 $aModernist literature$94119389 997 $aUNINA LEADER 03802nam 22005655 450 001 9910349444503321 005 20200702220551.0 010 $a3-030-19918-5 024 7 $a10.1007/978-3-030-19918-0 035 $a(CKB)4100000009191104 035 $a(DE-He213)978-3-030-19918-0 035 $a(MiAaPQ)EBC5928083 035 $a(PPN)258875798 035 $a(EXLCZ)994100000009191104 100 $a20190903d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEfficacy Analysis in Clinical Trials an Update $eEfficacy Analysis in an Era of Machine Learning /$fby Ton J. Cleophas, Aeilko H. Zwinderman 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XI, 304 p. 295 illus., 44 illus. in color.) 311 $a3-030-19917-7 327 $aPreface -- Traditional and Machine-Learning Methods for Efficacy Analysis -- Optimal-Scaling for Efficacy Analysis -- Ratio-Statistic for Efficacy Analysis -- Ratio-Statistic for Efficacy Analysis -- Complex-Samples for Efficacy Analysis -- Bayesian-Networks for Efficacy Analysis -- Evolutionary-Operations for Efficacy Analysis -- Automatic-Newton-Modeling for Efficacy Analysis -- High-Risk-Bins for Efficacy Analysis -- Balanced-Iterative-Reducing-Hierarchy for Efficacy Analysis -- Cluster-Analysis for Efficacy Analysis -- Multidimensional-Scaling for Efficacy Analysis -- Binary Decision-Trees for Efficacy Analysis -- Continuous Decision-Trees for Efficacy Analysis -- Automatic-Data-Mining for Efficacy Analysis -- Support-Vector-Machines for Efficacy Analysis -- Neural-Networks for Efficacy Analysis -- Ensembled-Accuracies for Efficacy Analysis -- Ensembled-Correlations for Efficacy Analysis -- Gamma-Distributions for Efficacy Analysis -- Validation with Big Data, a Big Issue -- Index. 330 $aMachine learning and big data is hot. It is, however, virtually unused in clinical trials. This is so, because randomization is applied to even out multiple variables. Modern medical computer files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. This is the first publication of clinical trials that have been systematically analyzed with machine learning. In addition, all of the machine learning analyses were tested against traditional analyses. Step by step statistics for self-assessments are included. The authors conclude, that machine learning is often more informative, and provides better sensitivities of testing than traditional analytic methods do. 606 $aMedicine 606 $aStatistics 606 $aBiometry 606 $aBiomedicine, general$3https://scigraph.springernature.com/ontologies/product-market-codes/B0000X 606 $aStatistics for Life Sciences, Medicine, Health Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030 606 $aBiostatistics$3https://scigraph.springernature.com/ontologies/product-market-codes/L15020 615 0$aMedicine. 615 0$aStatistics. 615 0$aBiometry. 615 14$aBiomedicine, general. 615 24$aStatistics for Life Sciences, Medicine, Health Sciences. 615 24$aBiostatistics. 676 $a006.31 676 $a615.50724 700 $aCleophas$b Ton J$4aut$4http://id.loc.gov/vocabulary/relators/aut$0472359 702 $aZwinderman$b Aeilko H$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910349444503321 996 $aEfficacy Analysis in Clinical Trials an Update$92115707 997 $aUNINA