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  • Computation and Close Reading
  • James E. Dobson
Eve, Martin Paul. Close Reading with Computers: Textual Scholarship, Computational Formalism, and David Mitchell’sCloud Atlas. Stanford: Stanford UP, 2019. Pp. 272.

The emergent field of computational literary studies has made some of its biggest claims through the rhetorical force of numbers—numbers being not just the quantification processes that make data out of text, but the sheer number of texts analyzed in typical studies. Distant reading, to cite the title of Franco Moretti’s 2013 book, initially presented itself as an alternative to close reading that would enable largescale analyses of the literary archive, heretofore unimaginable by individual literary scholars. Computational literary studies and other formulations of these methodologies–other proposed names include computational formalism or quantitative literary studies–emerged through the “big data” discourse of the first two decades of the twenty-first century. Big data, like any academic or corporate fashion, is more a discursive construct than a concrete set of practices and approaches. This particular discourse–one that often invokes industry and university partnerships, interdisciplinary and research team efforts, expensive shared resources, the possibilities of funding from governmental agencies, and more–offered literary scholars a way to participate in the epochal shifts that have already altered numerous research methods and created new subfields in biology, chemistry, psychology, among many other areas of study. Participating in this shift would enable scholars to pursue literary questions at multiple scales and to recover lost institutional prestige and rebuild connections with other fields, especially those in the social sciences that had once also employed close reading but have increasingly transitioned to quantitative methods.

The creation of large archives or libraries of digitized texts made the big data [End Page 405] discourse finally compatible with literary studies. These archives, which were primarily products of already-existing big data efforts within the commercial space, finally presented to computational literary studies an appropriate object for large-scale computation. It was thus literary history, rather than the study of literary works as such, that has become the dominant mode of analysis in computational literary studies. It was this methodological shift, made possible by the marriage of big data techniques and machine learning with well-organized collections and digital libraries, that brought digital humanities, and especially computational literary studies, to the foreground. This environment and its new discursive context changed the reception of methods that were once regarded as potentially interesting but too marginal to represent any broader “turn” in literary studies. Once literary studies was able to fully take part in the computational present, to attract interest from a range of participants including graduates and even undergraduates, to enable partnerships with computer scientists and the new class of workers known as data scientists, the situation had changed and these approaches, once termed “humanities computing,” were suddenly taken seriously. Prior attempts, which had generally been directed toward single works or single authors for both technical and field norm reasons, never gained much traction. Computational literary studies has emerged with some of the same quantitative methods in hand, but with the changed discursive situation it has now been received as a more serious endeavour.

In his now canonical definition of distant reading, especially as this loosely construed reading methodology relates to his larger project on world literature, Moretti makes it clear that his use of distance concerns the costs of the generation of knowledge about literature and literary history and not necessarily a metaphorical relation to the text as such:

Distant reading: where distance, let me repeat it, is a condition of knowledge: it allows you to focus on units that are much smaller or much larger than the text: devices, themes, tropes–or genres and systems. And if, between the very small and the very large, the text itself disappears, well, it is one of those cases when one can justifiably say, Less is more. If we want to understand the system in its entirety, we must accept losing something. We always pay a price for theoretical knowledge: reality is infinitely rich; concepts are abstract, are poor.

(Moretti 48–49; emphasis in original)

Moretti thus conceptualizes distance as not just the aggregation of texts...

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