Jo argues that a world awash in text requires interpretive tools that traditional quantitative science cannot provide. Text mining is dangerous because analysts trained in quantification often lack a sense of what could go wrong when archives are biased, incomplete, or evidence the suppressions of the past. Jo's talk will review a brief catalogue of disasters created by data science experts who voyage into humanistic study.
It finds a solution in “hybrid knowledge,” or the application of historical methods to algorithm and analysis. Case studies engage recent work from the philosophy of history (including Koselleck, Erle, Assman, Tanaka, Chakrabarty, and others) and investigate the “fit” of algorithms with each historical frame of reference on the past.
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