Our Researcher Spotlight Series profiles DataLab affiliated faculty, staff, graduate students and postdoctoral scholars across disciplines to demonstrate the diversity of our community and highlight their amazing work.

Amber Boydstun, Professor of Political Science

Amber Boydstun is faculty in Political Science at UC Davis and calls herself a “proud member” of DataLab’s Faculty Advisory Board. A scholar of political communication, she uses data science to bridge the gap between Communications and Political Science fields of research. 

Amber first became involved with DataLab a few years ago when she needed some help visualizing a large dataset. She worked with Duncan Temple Lang, Pamela Reynolds, and other DataLab staff and postdocs to find new ways to visualize her data. “They allowed me to see the data for the first time,” she says, “and it was glorious.” From there, she was hooked. “This genuinely is the magic of DataLab; it gives people an infrastructure for bridging between fields and between methods in ways that we don’t otherwise have. And that leads us to ask questions we wouldn’t have asked otherwise.”

Data science has shifted the conversation in Amber’s intersection of disciplines, as it has changed the questions that she is able to ask—not only the same kinds of questions on a bigger scale. She credits a small conference about text as data for offering her a turning point in her career; at the time, using text as data was “a relatively new concept” in Political Science, and while she had been utilizing textual data (in the form of 40,000 news stories) to write her dissertation, at the time it was standard to hand-code them. “That was just what you did,” she outlines. “If you wanted to track what news coverage was about, you just coded it by hand, you did it really carefully, and you paid undergrads a lot of money. You had to get a grant and buy them pizza,” she laughs. So the ability to mine, code, and analyze large amounts of textual data without all of the funds and time has really changed how things can be done. By way of an example, Amber notes how plagiarism detection algorithms have been used recently to track the development of bills like the Affordable Care Act, and researchers can now use computational tools and methods to see how much of the passed bill is in fact recycled from other bills. In the case of the ACA, nearly as much of the bill comes from previous Republican-sponsored bills as he Democrat-sponsored ones, which Amber notes “suggests…at least in terms of the meat and bones of policy, it really is a collaborative and more centrist kind of political system” here in the US: something we wouldn’t see as clearly or easily without the ability to use these methods. 

This genuinely is the magic of DataLab; it gives people an infrastructure for bridging between fields and between methods in ways that we don’t otherwise have.

Amber notes that she stays grounded by cultivating a diverse community and intentionally connecting with others outside of the academy, and as a result her work makes a huge impact both on her field and outside of the institution.  When she talks to students and others outside of institutions like Davis, she is able to help them understand the constraints and incentives of the systems in which they operate and encourage them to question why and how media covers particular political news stories. “I just want to explain [these concepts] so my mom knows why she should care about it,” she says. “Staying grounded in these relationships…watching the political world through the eyes of my partner’s kids,” for instance, “has been really transformative in how I think about politics.”