According to Hugh Craig and Brett Greatley-Hirsch in Style, Computers, and Early Modern Drama (2017), “The “arrival of machine-readable texts” and “of computational tools offers new ways to write a systematic” account of Renaissance drama by providing the “scale, which is needed for a broad literary history.” That digital technology provides the basis for a new and improved literary history is a bold assertion, but the same claim has been developed in several other influential studies recently, including Matthew Jockers’s Macroanalysis (2013), Ted Underwood’s Why Literary Periods Mattered (2013) and Distant Horizons (2019), and Andrew Piper’s Enumerations (2018). Underwood and Piper figure prominently along with Craig and Greatley-Hirsch in the first three sections of this essay, where I argue that the quantitative analysis of large textual corpora cannot, as they claim, put literary history on a more secure foundation. The gist of my argument is that the processing of data and the interpretation of texts are distinct activities, and that “the metaproblem of the digital humanities,” how to “get from numbers to meaning,” is insoluble. But it’s not just that the models of literary history offered by quantitative practitioners fail to deliver the benefits they promise; these benefits – reducing if not eliminating the “dilemmas of bias and selectivity” and the “extreme subjectivity of interpretation” – are not really beneficial. In the final section, I sketch out more useful model for understanding literary history, one based on controversy and persuasion rather than “congruence” and demonstration, designed more to generate than to regulate the interpretation of literary texts.
DOI: | https://doi.org/10.37307/j.1866-5381.2020.02.05 |
Lizenz: | ESV-Lizenz |
ISSN: | 1866-5381 |
Ausgabe / Jahr: | 2 / 2020 |
Veröffentlicht: | 2020-11-24 |
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