Find below a list of my peer-reviewed journal publications. Where applicable, Google Scholar, professional websites, or faculty profiles are linked.
Please email me if you'd like a more complete list, including works under review or in progress.
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This article presents a computational approach to examining immigrant incorporation through shifts in the social “mainstream.” Analyzing a historical corpus of American etiquette books, texts from 1922-2017 describing social norms, we identify mainstream shifts related to longstanding groups which once were and may currently still be seen as immigrant outsiders in the United States: Catholic, Chinese, Irish, Italian, Jewish, Mexican, and Muslim groups. The analysis takes a computational grounded theory approach, combining qualitative readings and computational text analyses. Using word embeddings, we operationalize the chosen groups as focal group concepts. We extract sections of text that are salient to the focal group concepts to create group-specific text corpora. Two computational approaches make it possible to examine mainstream shifts in these corpora. First, we use sentiment analysis to observe the positive sentiment in each corpus and its change over time. Second, we observe changes in each corpus’s position on a semantic dimension represented by the poles of “strange” and “normal.” The results indicate mainstream shifts through increases in positive sentiment and movement from strange to normal over time for most of the group-specific corpora. These research techniques can be adapted to other studies of social sentiment and symbolic inclusion.
Social scientists of class and inequality have documented the rise of omnivorousness, informality, ordinariness, and emphasis on meritocracy. This apparent decline in class closure contrasts sharply with rising inequality and declining economic mobility. How are these competing developments reflected in everyday class distinction-making? In this article, we answer this question by applying Goffman's work on the symbols of class status to the analysis of unique data: a corpus of etiquette books published between 1922 and 2017. We use word embeddings to quantify the salience of six class concepts (affluence, cultivation, education, employment, morality, and status) in the corpus. We find that education and employment are increasingly salient while status, affluence, cultivation, and morality decline in their salience to class distinction-making. These results signal a decline of class operating as a status group through cultural closure, the rise of education and employment as the carriers of class in everyday life, and the corresponding legitimation of class position and class inequality based on supposedly meritocratic grounds. This research opens up new avenues for studies of class and the application of computational methods to investigations of social change.
This study examines the effects of community vulnerability on residential sequestering across counties in the United States. Powerlessness and racialized politics are two hypothesized reasons for why community vulnerability affects social distancing behavior. Powerlessness is tied to the socioeconomic disadvantages of places, which intertwines with politics and race to produce a stratified response to the pandemic. We examine these dynamics with analyses that account for the disease epidemiology and other demographic factors. Data come from multiple sources, including Google’s Mobility Reports and Cuebiq’s Mobility Insights. Growth curve analyses find that socioeconomic disadvantage, political orientation, and racial composition independently explain the rate of change in mobility and peak residential sequestering levels during the initial outbreak. These conceptually separate dimensions of community vulnerability operate in concert, rather than as substitutes or as competing explanations, to impact the behavioral response to COVID-19
Access to emergency funds during times of need is increasingly dependent on a “choice-based” retirement system in the United States. This study examines the operation of this choice-based retirement system across a socioeconomically stratified population impacted by the Great Recession. Using data from the Panel Study of Income Dynamics collected between 2005 and 2011, we first assess how socioeconomic status shapes access to different types of retirement plans. We then examine the varied pathways that lead to the early withdrawal of retirement savings. The results depict a multistage selection process through which socioeconomic resources structure the process of withdrawal. This occurs first through the availability of retirement funds and the makeup of the retirement portfolio and then through the likelihood of hardship. The results also reveal the importance of education in getting access to retirement savings during a hardship. The general implications for choice-based programs are discussed.
This article investigates partisan beliefs regarding attributions of responsibility for mental illness and support for mental health treatment. In study 1, we utilize a nationally representative data set to investigate these relationships with respect to generalized anxiety disorder. In study 2, we utilize an online convenience sample to assess these relationships in the context of schizophrenia. In both studies, Republicans were more likely than Democrats to attribute mental health disorders to factors that lie within patients’ control and were less supportive of healthcare coverage. In addition, given the rhetorical, erroneous link between schizophrenia and gun violence, we assess participants’ beliefs about gun control in the context of mental health. Paradoxically, we find that people who support gun rights for the mentally ill are the least likely to support healthcare coverage for the mentally ill. We discuss the implications of our findings for shaping U.S. gun debates.