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How to Respond to Data Science: Early Data Criticism by Lionel Trilling

Authors: Brian Beaton;

How to Respond to Data Science: Early Data Criticism by Lionel Trilling

Abstract

Abstract This article was originally drafted just four weeks after the publishing of Dataclysm, a 2014 book by Christian Rudder that sought to popularize data and data science by, in part, dismissing the social sciences and humanities as obsolete approaches to knowledge production. In looking for a potential way of responding to data scientists like Rudder, this article examines a 1948–50 essay about data that was written by Lionel Trilling (1905–75). Trilling frames data as part of our broader cultural history, which includes literature, drama, epic poetry, and the arts. This article argues that what Trilling models in the essay is a line of writing and thinking about data—a type of data criticism—that today offers tremendous promise for responding to data science and to evangelists like Christian Rudder.

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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
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