Department

English

Date of Paper/Work

Spring 2018

Degree Name

Master of Arts in English (M.A.)

Type of Paper/Work

Essay

Advisors

Dr. Alexis Easley, Dr. Amy Muse, Dr. Paul Fyfe

Abstract

The rapid rise of the digital humanities over the past decade has transformed literary study, helping scholars to discern broader patterns in print culture and media history. Engaging with methodologies such as data mining, macroanalysis, and network analysis, my Master's Essay utilizes these computational analytics approaches in order to address longstanding critical questions in women's literary history - in particular, how might these tools help us understand the crucial rise of women's networks during the long nineteenth century. To what extent were women's relationship s with fellow female authors important to their success in a male-dominated publishing marketplace, and what new insights are gained from viewing these relationships on a macro-analytic level, rather than simply viewing the individual network or the network of "important" or "canonical" writers associated with a particular literary period or movement?

In taking a distant approach without privileging canonical authors over others, the macro-network I generated from mining bio-data of nearly 700 women writers becomes a fluid model from which new trails of scholarship can be mapped rather than a stagnant source of evidentiary support for pre-existing arguments. Utilizing networking software to track details of women's interactions with one another my essay reveals several surprising who functioned as crucial nodes in communities of women writers during the long nineteenth century - Joanna Baillie, Geraldine Jewsbury, and Margaret "Storm" Jameson - and offers an analysis of why this high connectivity has not translated into canonicity.

Of course, any database of women writers obscures as much as it reveals about women's experience as participants in gendered publication networks, and my essay closes with analysis and acknowledgement of the archival absences, gaps, and biases of human politics embedded in the data in- and exclusion process encoded within the construction of the Cambridge University's Orlando Project digital database, from which my information was mined.

Keywords

Literature

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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