I teach courses that explore the intersections of science, health, technology, gender, and data.
Current and past course offerings at UC Davis are listed below.
Algorithmic Reading: Computational Text Analysis in the Humanities and Interpretive Social Sciences
STS 205 • winter 2020As more texts become available digitally, computational textual analysis is becoming more common in the humanities and social sciences. While quantitative social scientists have readily added algorithmic reading methods to their computational repertoire, scholars in the humanities and interpretive social sciences have been more cautious. We now have many examples of algorithmic reading being used for empirical research by quantitative social scientists or by literary scholars who model their endeavors on the quantitative social sciences. We have also seen an outpouring of critical and skeptical takes on algorithmic reading from scholars in the humanities and interpretive social sciences. This course brings together the “yacking” of the humanities and the “hacking” of the social sciences. It is driven by the premise that effectively using computational tools to analyze texts requires critical engagement with those tools and that effectively critiquing those tools requires getting one’s hands dirty by working with them. It therefore takes a hands-on approach grounded in the field of science and technology studies (STS) to critically exploring these tools through active use of them. Using the R programming language, we explore a variety of methods for analyzing and visualizing texts. We consider how these modes of analysis can help us pose and explore valuable research question, and we critically interrogate these approaches and the results they produce. No prior experience necessary. In response to high levels of demand for this course, I have also developed a DIY version.
Texts, Maps, Networks, and Numbers: Computational Approaches in the Humanities and Interpretive Social Sciences
STS 205 • winter 2019This seminar introduced graduate students to computational approaches to data analysis in the humanities and interpretive social sciences. Using the R programming language, we will analyzed and visualized numeric, spatial, textual, and network data. We considered how these modes of analysis can help us pose and explore valuable research question, and we interrogated these approaches from a critical perspective grounded in science and technology studies.
Gender & Science
STS 150 • spring 2020This course offers an interdisciplinary approach to the relations between gender and science. It focuses on using reading and writing to explore two big questions: 1) Why have women (and LGBTQIA+ people and people of color) been excluded from the practice of science? 2) What have been the consequences of their exclusion for science and for society? Our approach to these questions is guided by two theoretical premises: 1) Science is a social activity 2) All knowledge is situated and therefore partial. Topics include the biological and cultural construction of sexual difference, the role of women as practitioners of science, and feminist approaches to science.
Health and Medical Technologies
STS 122 • winter 2021This course introduces students to critical social scientific approaches to health and medical technologies grounded in the interdisciplinary field of science and technology studies (STS). It takes a historical approach to the topic, encouraging students to think about the ways in which the past shapes the present. It fulfills the Domestic Diversity requirement and therefore foregrounds the role of medical technologies in the production, maintenance, and contestation of various forms of social difference, notably race, gender, sexuality, and disability.
Visualizing Society With Data
STS 112 • winter 2021Data visualization is an important mode of analysis and communication today, particularly for investigating and conveying information about society and social change. This course will focus on using the R statistical programming language to analyze and visualize social change over time using individual-level data from the United States Census (1870-2010). Through this practice, students become very familiar with a large and versatile source of data (the Integrated Public Use Microdata Series) and learn critical approaches to working with data that they can apply to other data sets as well. The course will examine why and how the data were produced, what material effect they had and continue to have on the world, what information we can glean through their analysis, and how to visually represent various kinds of historical change, such as race formation, internal and international migration, and industrialization. Assignments will give students practice not only with data management, analysis, and visualization, but also with communicating their analysis, results, and interpretation in writing. This course fulfills the Domestic Diversity requirement. We will analyze census data to understand the historical processes involved in producing and challenging social inequality along the axes of race, gender, class, and sexuality. Most assignments will require that you grapple with at least one of these dimensions of diversity. This course also fulfills the Quantitative Literacy requirement. Students will gain competency and comfort in working with numerical data and evaluating arguments made on the basis of numerical data. By using historical data to explore social change, students will learn to generate and communicate arguments about numerical data collected to investigate U.S. society.
Data Feminism
fRs 004 • spring 2020This course was developed with Professor Lindsay Poirier as part of our Data Feminism working group during the 2019-2020 academic year. It explores the ubiquity of data in modern life and the way that objectivist language surrounding data obscures its role in reproducing gender inequality and other forms of social inequity. Students will be introduced to the basics of data generation and analysis and concepts from feminist theory such as intersectionality, standpoint epistemology, and situated knowledge. Students will create and visualize their own datasets to consider how complex phenomena get reduced to fit standard categories in the processes of counting and classifying the world. Students will also learn to think critically about objectivity, the gendering of data work, and the role of data in propagating oppression.