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Digital scholarship is an incredibly awkward term that people have come up with to describe a complex group of developments. The phrase is really, at some basic level, nonsensical. After all, scholarship is scholarship. Doing science is doing science. We don't find the Department of Digital Physics arguing with the Department of Non–Digital Physics about who's doing "real" physics.

Soon, people wanted to start talking more broadly about newly technology-enabled scholarly work, not just in science; in part this was because of some very dramatic and high-visibility developments in using digital technology in various humanistic investigations. To do so, they came up with the neologisms we enjoy today—awful phrases like e-scholarship and digital scholarship.Having said that, I do view the term digital scholarship basically as shorthand for the entire body of changing scholarly practice, a reminder and recognition of the fact that most areas of scholarly work today have been transformed, to a lesser or greater extent, by a series of information technologies: High-performance computing, which allows us to build simulation models and to conduct very-large-scale data analysis Visualization technologies, including interactive visualizations Technologies for creating, curating, and sharing large databases and large collections of data High-performance networking, which allows us to share resources across the network and to gain access to experimental or observational equipment and which allows geographically dispersed individuals to communicate and collaborate; implicit here are ideas such as the rise of lightweight challenge-focused virtual organizations

We now have enormous curated databases serving various disciplines: GenBank for gene sequences; the Worldwide Protein Data Bank for protein structures; and the Sloan Digital Sky Survey and planned successors for (synoptic) astronomical observations. All of these are relied upon by large numbers of working scientists. Yet the people who compiled these databases are often not regarded by their colleagues as "real" scientists but, rather, as "once-scientists" who got off-track and started doing resource-building for the community. And it's true: many resource-builders don't have the time to be actively doing science (i.e., analysis and discovery); instead, they are building and enabling the tools that will advance the collective scientific enterprise in other, less traditional ways. The academic and research community faces a fundamental challenge in developing norms and practices that recognize and reward these essential contributions.This idea—of people not doing "real" research, even though they are building up resources that can enable others to do research—has played out as well in the humanities. The humanists have often tried to make a careful distinction between the work of building a base of evidence and the work of interpreting that evidence to support some particular analysis, thesis, and/or set of conclusions; this is a little easier in the humanities because the scale of collaboration surrounding emerging digital resources and their exploitation for scholarship is smaller (contrast this to the literal "cast of thousands" at CERN) and it's common here to see the leading participants play both roles: resource-builder and "working" scholar.

To ask about the field is really to ask how or what DH knows, and what it allows us to know. The answer, it turns out, is not much. Let’s begin with the tension between promise and product. Any neophyte to digital-humanities literature notices its extravagant rhetoric of exuberance. The field may be "transforming long-established disciplines like history or literary criticism," according to a Stanford Literary Lab email likely unread or disregarded by a majority in those disciplines. Laura Mandell, director of the Initiative for Digital Humanities, Media, and Culture at Texas A&M University, promises to break "the book format" without explaining why one might want to — even as books, against all predictions, doggedly persist, filling the airplane-hanger-sized warehouses of Amazon.com.

A similar shortfall is evident when digital humanists turn to straight literary criticism. "Distant reading," a method of studying novels without reading them, uses computer scanning to search for "units that are much smaller or much larger than the text" (in Franco Moretti’s words) — tropes, at one end, genres or systems, at the other. One of the most intelligent examples of the technique is Richard Jean So and Andrew Piper’s 2016 Atlantic article, "How Has the MFA Changed the American Novel?" (based on their research for articles published in academic journals). The authors set out to quantify "how similar authors were across a range of literary aspects, including diction, style, theme, setting." But they never cite exactly what the computers were asked to quantify. In the real world of novels, after all, style, theme, and character are often achieved relationally — that is, without leaving a trace in words or phrases recognizable as patterns by a program.

Perhaps toward that end, So, an assistant professor of English at the University of Chicago, wrote an elaborate article in Critical Inquiry with Hoyt Long (also of Chicago) on the uses of machine learning and "literary pattern recognition" in the study of modernist haiku poetry. Here they actually do specify what they instructed programmers to look for, and what computers actually counted. But the explanation introduces new problems that somehow escape the authors. By their own admission, some of their interpretations derive from what they knew "in advance"; hence the findings do not need the data and, as a result, are somewhat pointless. After 30 pages of highly technical discussion, the payoff is to tell us that haikus have formal features different from other short poems. We already knew that.