This Element tackles the problem of generalization with respect to
text-based evidence in the field of literary studies. When working
with texts, how can we move, reliably and credibly, from individual
observations to more general beliefs about the world? The onset of
computational methods has highlighted major shortcomings of
traditional approaches to texts when it comes to working with small
samples of evidence. This Element combines a machine learning-based
approach to detect the prevalence and nature of generalization
across tens of thousands of sentences from different disciplines
alongside a robust discussion of potential solutions to the problem
of the generalizability of textual evidence. It exemplifies the way
mixed methods can be used in complementary fashion to develop
nuanced, evidence-based arguments about complex disciplinary issues
in a data-driven research environment.
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