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.
Emily Klancher Merchant is Associate Professor of Science and Technology Studies at UC Davis. She published her first book in 2021 titled Building the Population Bomb, and her current book project is titled Molecular Eugenics: the American Pursuit of Intelligence Genes.
Emily likes patterns. She is currently hard at work, crocheting twenty-five hats for her daughter’s upcoming school fundraiser. Playing with color and texture, Emily says that her interest in knitting, crocheting, and other pattern-oriented crafts is “related to my personality in a way. As someone who’s interested in quantitative things, it is fun to see all of the different things that can be made by mathematical combinations and orderings.”
Emily finds patterns not only in her crafting, but also in her research. As a historian of science, she is particularly interested in the creation and development of ideas within quantitative social sciences. Recently, her pattern-oriented thinking is focused on “the ways the field constructs the social world by classifying it, sometimes intentional and sometimes unintentional.” These classifications have patterns, not unlike the mathematical orderings of crocheted hats, and Emily is concerned with how these classification systems persist in our history of quantifying and comparing people:
“In order to quantify things and compare them, you have to classify them, and classification of human beings opens up a whole can of worms. What do these classifications mean? How do these classifications impact our daily lives? When you classify people, you classify them into the categories that currently exist. And in doing so, you’re often unwittingly upholding the status quo.”
I need to keep myself open to being surprised and to proving myself wrong.
To follow patterns across time, Emily combines archival research, oral history interviews and computational text analysis. The latter makes her research possible, since Emily realizes that “there are things that I couldn’t read, no matter how much time I had.” Using computational text analysis tools like topic modeling, Emily can draw out patterns from vast amounts of data, patterns that are not necessarily apparent from just reading through materials once or twice.
Importantly, Emily doesn’t know what she will uncover in her research, and she makes sure she’s not set on one specific pattern, in what could be a pernicious self-fulfilling prophecy. “I need to keep myself open to being surprised and to proving myself wrong,” she says. Between all of her research methods, new patterns can abruptly appear, and Emily often does not know the conclusion of her research until the very end. Though seemingly like a frustratingly convoluted mess of tangled patterns—a crochet hat that might also be a sock or a scarf—Emily finds that the unknown is a necessary part of the research. “If I knew what story that I was going to tell in advance, there would be no point in going to the trouble of doing all of the research.”
Emily knows patterns are not objective, as they require perspective to detect and follow, just as she creatively interprets the patterns for her daughter’s crochet hats. When Emily shares her research, she embraces these creative elements of her work because she believes that they ultimately make her research more useful in troubling historical categories rather than solidifying them.
You can see more of Emily’s work on her personal website.