This presentation extracts the basic analytic methods visible in text mining or cultural analytics and in the long-form argument. Key to the three different methods examined in this paper are “samples” and “case histories”: distant readers aspire to work with larger and larger sample sizes of literary and historical documents; surface and close readers work with individual cases, specific texts or sets of texts written in the past, either describing them (surface) or analyzing them deeply. I present three readings of work by Jane Austen: distant, surface, and depth. I then describe results of my own analyses using the The Jane Austen Fiction Archive of carefully encoded documents, a state-of-the-art digital edition (http://www.janeausten.ac.uk/index.html). Careful encoding using TEI guidelines, I argue, helps bridge the gap between “samples” and “case histories,” offering scholars the opportunity to perform the best scholarship at scale.
Laura Mandell is the author of Breaking the Book: Print Humanities in the Digital Age (2015), Misogynous Economies: The Business of Literature in Eighteenth-Century Britain (1999), a Longman Cultural Edition of The Castle of Otranto and Man of Feeling, and numerous articles primarily about eighteenth-century women writers. An article in New Literary History, “What Is the Matter? What Literary History Neither Hears Nor Sees,” describes how digital work can be used to conduct research into conceptions informing the writing and printing of eighteenth-century poetry. She is Project Director of the Poetess Archive, an online scholarly edition and database of women poets, 1750-1900 ( http://www.poetessarchive.org), Director of 18thConnect (http://www.18thConnect.org), and Director of ARC (http://www.ar-c.org), the Advanced Research Consortium overseeing NINES, 18thConnect, and MESA. Her current research involves developing new methods for visualizing poetry, developing software that will allow all scholars to deep-code documents for data-mining, and improving OCR software for early modern and 18th-c. texts via high performance and cluster computing.
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