In recent years, the label “cultural analytics” (CA) has risen in prominence as a catchall for the application of data-driven, computational, and/or quantitative tools and methods to revisit research areas associated with the study of culture in various disciplines. The label's first use is credited to Lev Manovich, but its use has arguably grown beyond his original definition (Manovich 2016; Manovich 2020; Piper 2016). Although CA has been described as part of the “big tent” of digital humanities (DH), it operates as both a subfield within DH and an emerging field of its own with constituencies in the humanities, the social sciences, and information & computer science. Ted Underwood has gone as far as arguing that CA must be acknowledged as “a bridge between the humanities and quantitative social science” that belongs equally to both (Underwood). Alternative labels for the focal area (e.g. computational humanities, computational literary studies, distant reading, and macroanalysis) have done comparatively less to signal this confluence of multiple constituencies (see Da, Jockers, Manovich 2020, Michel et al., and Moretti). Whatever title is used, debates around CA have tended to focus on: (1) applying computational methods with sufficient quantitative rigor; (2) preserving humanities sensibilities in the face of methods that may appear to require embracing a positivist and overly reductive worldview; and (3) questioning whether quantitative and computational methods can deliver results of interest and relevance to the humanities (See Bode, Da, and Levy-Eichel & Scheinerman). All three of these topic areas suggest a clash of competing values, with one set of stakeholders typically emphasizing item 1 and another set of stakeholders typically emphasizing items 2 and 3. Failing to address this dynamic directly has slowed the progress of a core area of concern for CA: formalizing how aspiring CA practitioners enter the field. As Underwood has described, a lack of formal training for humanists in data-driven or quantitative inquiry creates a structural inequality whereby practitioners with a background in these areas tend to have a competitive advantage and feel more welcome in the field. This context creates obstacles for both established humanities researchers hoping to move into CA and graduate students who want to specialize in CA approaches.
In this long paper presentation, I address the challenges of addressing this structural inequality, as well as areas where I believe progress can be made. My central argument is that CA must work toward identifying core methodological areas (competencies) and work toward optimizing the order in which specific topics are taught (sequencing). Although this process will likely be contentious (and may read as hostile to the idea of DH’s “big tent”), it has become increasingly clear that this process is overdue. Competencies designated as core to CA will not be equally valuable to all DH practitioners, but acknowledging this fact need not precede an argument to excise CA from DH or otherwise divorce the two from one another. My paper is divided into three sections: (1) summary of CA’s current socio-technical infrastructure; (2) overview of well-functioning formalization processes; and (3) recommendations for the future of CA curriculum.
In this section, I describe the state of CA curriculum and sequencing, which has developed in the absence of the relatively well-functioning formalization processes for field entry that can be found in comparable quantitative disciplines. This summary is based on surveying publicly available syllabi and academic program websites to ascertain norms such as:
Although I have found that many course materials are not available online (and others may be available but hard to find), attempting to answer these questions is crucial to understanding the de facto state of CA curriculum, as well as the values that appear to be informing current practices.
I supplement this section with a literature review of scholarship relevant to or directly engaged with questions of CA training. This task is more complicated than a typical literature review, however, as it has involved locating publications in a broad range of publications, and evaluating a diverse range of material for both relevance and quality. The most relevant DH and CA materials tend to fit into one of three categories:
This section offers a fourth area for relevant course materials and scholarly literature that is often under-appreciated: curricular and pedagogical materials from analogous disciplines (e.g. computer science, applied mathematics, computational social sciences, econometrics, etc.), as well as materials documenting the processes by which these disciplines have formalized areas of core competency.
This section is informed by my research in the field of CA, my experience teaching data analytics (DA) in an interdisciplinary program at Denison University. Denison University is a diverse, highly selective, residential liberal arts college (undergraduate students only) enrolling approximately 2,300 students. The DA program has faculty with training in applied statistics, applied mathematics, astrophysics, computational biology, cultural analytics, and econometrics. Coursework in the major includes core DA courses, math and computer science prerequisites, several courses cross-listed with math and computer science, and a subject area concentration of 3-4 courses in an outside department (“Degree Requirements”) In this context, I have taught courses across the DA program; adapted CA topics and assignments into other data analytics and computer science classes; and piloted an undergraduate elective called “Introduction to Cultural Analytics” (Lavin).
Locating CA as a confluence of stakeholders from multiple backgrounds allows questions of competencies, sequencing, and synthesis to come to the foreground. In Underwood’s words, CA requires learning “elements of mathematics, computer programming, and quantitative research design” (Underwood). It has not been my experience, however, that CA can simply appropriate mathematics, computer science, and quantitative social science training from other disciplines; rather, a genuinely interdisciplinary, multi-institutional intervention is called for. As the discipline formalizes, it can look to its closest counterparts for examples and templates of the kind of socio-technical infrastructure that would best preserve inclusiveness while simultaneously facilitating a formal process by which competency to the enter the field is enabled and evaluated. Achieving this goal requires establishing processes by which such outputs are created and maintained; fostering constructive discussions among stakeholders; and developing criteria by which competing stakeholder values can be reconciled or synthesized.
This section of my paper serves as a conclusion and a call to action. It attempts to synthesize import takeaways from the previous two sections, namely: