The Invisible Work of the Digital Humanities Lab: Preparing Graduate Students for Emergent Intellectual and Professional Work
This article illuminates the ways that digital humanities labs might foster experiences for graduate students that fulfill what Alexander Reid (2002) postulates as the "central task" of the digital humanities graduate education. We argue that while the digital humanities lab as an institutional economic model does not necessarily promote a focus on graduate student professionalization, it uniquely has the capacity to push back against competing discourses of neoliberal vocationalism, funding and labor precarity on one hand, and technological utopianism and tool fetishization on the other, to train students agile, contextual, and rhetorical mindsets with which to enter technologically mediated workplaces and lives. To begin, we review the discussion of digital humanities labs in the literature: digital humanities institutional models, how these models are practiced, lab funding, and the resultant position of labs as sites of training for graduate students. From there, we offer a teaching case from the Lab's fall 2015 "Stories from Data" workshop in order to render visible a set of principles to guide professionalization of graduate students in the digital humanities lab. We conclude with reflections on how these principles might alter current discussions of the success or failure of the Mellon Foundation and NEH ODH digital humanities funding initiatives in the United States. Introduction: The Visible Work of a DH Lab In The Big Humanities: Digital Humanities/Digital Laboratories, Richard J. Lane discusses a number of types of "humanistic labs, centres, projects, and products," but he maintains a striking focus on the possibilities of the digital humanities (DH) lab. For Lane, the digital humanities lab provides an opportunity to bring a Big Science research model to the humanities, which includes a shift to "lab based" knowledge production [Lane 2017] Everything is larger: the research teams, the data sets, the duration of projects, the funding evolution, so to speak, of what we might call a "lab ethos" [Lane 2017] In addition, the construction of Big Humanities as a topic area correlates closely with a shift in philanthropy in higher education, which is "less concerned about creating in perpetuity funds than solving a large, intractable problem" [Thorp 2010, 145] DH funders such as the Mellon Foundation will fund projects for impact, which is viewed as highly interdisciplinary, with high levels of engagement, and market consciousness [Thorp 2010] The more scientific the model and technical the discourse, the more likely STEM funding may also be available [Thornham 2017] For this reason, Big Humanities are in great demand, and labs compete for large scale funding through the constructive and imagineering nature of their grants. Inevitably, the paradigm of funding shapes the kind of work that is done, and the tools and methods that are used. Of course this is not the only value that institutions see in digital humanities labs. One need only inspect the technologies in a given DH lab to know what kinds of projects in which the lab is participating or able to support. A large, funded project will support the tools and methods needed to get the specific work of that project done. There may even be development of specific packages or software. By contrast, a lab that fits the computing support model will offer a broader range of technologies, tools, and personnel to fit a broad range of DH interests. An example of the former can be found in the humanities college funded Digital Environmental Humanities Lab at California State UniversityNorthridge, where students under the mentorship of one professor used XMIND software to visualize data related to the
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