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Sarah Allison

Assistant Professor


Ph.D., Stanford University, 2012


  • College of Arts and Sciences
  • Center for Editing and Publishing
  • English


Sarah Allison received her PhD from Stanford University in 2012.  She specializes in Romantic and Victorian literature, with a particular focus on debates about the purpose of art--what writers in the period thought literature should teach, and how.  Her book project, Reductive Reading, reveals a counterintuitive truth about criticism: that one of the most powerful ways to generate subtle reading is to be reductive; that is, to design projects with the questions up front, with a clear statement of how we propose to find the answers. This book is a manifesto for and a model of how digital analysis can provide daringly simple approaches to complex literary problems.

Her research combines close reading at the level of the sentence with digital searches that trace patterns across large bodies of work.  She has co-authored three pamphlets on quantitative studies of literary style with the Stanford Literary Lab, two of which were subsequently reprinted in n+1.  Her article, “George Eliot’s Discerning Syntax” has been published in ELH, and an essay on fact and fiction in Elizabeth Gaskell’s biography of Brontë is forthcoming in Genre in 2017, which is part of a second project tentatively titled True Fiction, a book on fictionality in biographies of the figures Charlotte Brontë, Charles Dickens, and Hermione Lee.  She has also published in the Studies in the Novel-affiliated site Teaching Tools: Digital Humanities and the Novel, the essay collection Airplane Reading (Zero Books, 2016), and the book review section of the New Orleans Review As part of her developing scholarly interest in the circulation of Swedish texts in translation, she has recently affiliated with the Section on the Sociology of Literature at Uppsala University, Sweden. 

Classes Taught

Areas of Expertise

  • Victorian Literature
  • Romanticism
  • Poetry
  • Literary Theory
  • Digital Humanities Methods