Research

Re-Landscaping Citation Analytics

My current research proposes a shift in how we visualize academic fields: from networks of cited sources to disciplinary terrain.

I use co-citation methods from bibliometrics to map research traditions across disciplinary areas based on what work is commonly cited together. I focus most on interdisciplinary scholarship that otherwise distinct communities cite in common and that raw citation counts may underrepresent.

Such maps of co-citation usually render a field as a “hairball” of nodes and connecting lines. At large scales, these diagrams require thresholding to be understood, and the distances between points are artifacts of the layout algorithm rather than anything about the citations themselves.

I instead recover an early approach that treats citation density as elevation. Peaks are areas of dense co-citation. The valleys between them are where interdisciplinary work bridges closely related clusters, and where shared but not frequently cited influences lie.

I draw here on Jacqueline Jones Royster’s metaphor of disciplinary landscaping. Royster reminds us that our intellectual maps are partial, shaped by our vantage points on disciplinary bedrock, and she calls us to consider how we showcase intellectual space from those vantages. This matters at a moment when information is increasingly accessed through black-boxed algorithms, from proprietary search engines to generative AI, where the ability to trace the provenance and relationship of ideas is obscured. Mapping a field as terrain can help us re-center scholarship elided by methods that boost already highly-cited work.

My Approach to Data Analysis

From my training as a digital humanist, I understand data analysis as an exploratory and iterative practice. It can corroborate what we may already know about our interests and assumptions, answer novel research questions we may have, and prompt further questions based on the patterns we see in the results from our situated vantages. Applied at different scales, data analysis can guide and augment rather than supplant close analysis. As I’ve argued in “Transforming Text,” for the Journal of Writing Analytics, working with data (cleaning, transforming, analyzing, and presenting it) is a means of writing just as writing is a means of analytical rearrangement.

Methods impart biases as much as data do. Even given complete and well-structured data, a chosen method makes some data tractable while other data is discarded. Adjusting a parameter shows some features while obscuring others. I work to make those choices, and my vantage on them, legible to readers.

Selected Publications

“Introduction to Issue Twenty-Seven: Minimalist Digital Humanities Pedagogy”, Themed Issue, with Patricia Belen, Stefano Morello, Danica Savonick, and Brandon Walsh, in Journal of Interactive Technology and Pedagogy, 27 (2025).

“Writing program assessment as a site for multi-generational mentoring: Building research trajectories for justice.” With Qianqian Zhang-Wu, Devon Regan, and Mya Poe, in Leigh Gruwell and Charles Lesh, eds., Mentorship/Methodology: Reflections, Praxis, and Futures. Utah State University Press, 2024.

Introduction, General Issue with Forum on Data and Computational Pedagogy, with Kelly Hammond and Brandon Walsh, in Journal of Interactive Technology and Pedagogy, 18 (2020).

“Transforming Text: Four Valences of a Digital Humanities Informed Writing Analytics,” in The Journal of Writing Analytics, Vol 1 (2017).