Department of Sociology

We examine whether the COVID-19 pandemic was associated with changes to daily activity limitations due to poor physical or mental health and whether those changes were different within and between gendered and racialized groups. We analyze 497,302 observations across the 2019 and 2020 waves of the Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance System survey. Among White men and women, the COVID-19 pandemic was associated with fewer days of health-related activity limitations and decreased frequent activity limitation (≥14 days in the past month) compared to the prepandemic period. By contrast, Latina and Black women experienced increased days of activity limitation and greater likelihood of frequent activity limitation, and these changes were significantly different than for White women. These findings are robust to the inclusion of structural inequality measures and demonstrate how systemic racism and sexism likely exacerbate a myriad of pandemic-related health problems.

The US is the world leader in imprisoning immigrants. Its mass immigration detention system emerged as an extension of mass incarceration, rooted in a legacy of racist US immigration and criminal laws. Immigration policy is a structural determinant of health that negatively affects the health of imprisoned immigrants, their families, and their communities. The systemic harms of detention facilities, which we refer to as immigration prisons, have been extensively documented, yet incrementalist reforms have failed to result in improved outcomes for immigrants. We argue that ending the practice of immigrant imprisonment is the most effective solution to mitigating its harms. Community-based programs are safer and less expensive than imprisonment, while also being effective at ensuring compliance with government requirements. We identify several priorities for researchers and policy makers to tackle the health inequities resulting from this structurally racist system. These include applying a critical, intersectional lens to studying the policies and practices that drive imprisonment, engaging affected communities in research and policy development, and creating an accountable and transparent system of data collection and release to inform health interventions. The reliance of the US on immigrant imprisonment is a policy choice with immense social and economic costs; dismantling it is critical to advancing health equity.

As postdoctoral training has become a requirement in many STEMM fields the influence of postdoc hiring on STEMM labor force inclusion and diversity has increased, yet postdoc hiring processes have received only limited attention from researchers. Drawing on status theory and data for 769 postdoctoral recruitments, we systematically analyze the relationship between gender, race-ethnicity, and postdoctoral hiring. The findings show: (1) differences by gender and race-ethnicity in application rates, and in whether an applicant is seriously considered, interviewed, and offered the postdoc position; (2) hiring disparities correlate with between-group differences in applicants' network connections, referrer prestige, and academic human capital; (3) between-group differences in network connections have the greatest power to account for hiring disparities; and (4) hiring processes may differ by applicant gender or race-ethnicity, the female representation in the STEMM field and the race of the search committee chair. We discuss competing interpretations of the results and highlight directions for future research.

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The interdisciplinary field of knowledge discovery and data mining emerged from a necessity of big data requiring new analytical methods beyond the traditional statistical approaches to discover new knowledge from the data mine. This emergent approach is a dialectic research process that is both deductive and inductive. The data mining approach automatically or semi-automatically considers a larger number of joint, interactive, and independent predictors to address causal heterogeneity and improve prediction. Instead of challenging the conventional model-building approach, it plays an important complementary role in improving model goodness of fit, revealing valid and significant hidden patterns in data, identifying nonlinear and non-additive effects, providing insights into data developments, methods, and theory, and enriching scientific discovery. Machine learning builds models and algorithms by learning and improving from data when the explicit model structure is unclear and algorithms with good performance are difficult to attain. The most recent development is to incorporate this new paradigm of predictive modeling with the classical approach of parameter estimation regressions to produce improved models that combine explanation and prediction.

While the deleterious impacts of parental incarceration are well documented, we know less about the experiences of children with parents imprisoned by immigration authorities. We draw from 62 multigenerational and multiperspective interviews conducted in California with school-age children experiencing parental immigration imprisonment (PII), and their nondetained caregivers. We find that children experiencing PII report feelings and behaviors suggestive of significant psychological distress, which leads to changes in engagement and behavior at school. While some children access academic support and counseling, often following advocacy from nondetained parents or interventions by teachers, others do not receive such support. Many children conceal their family’s situation and withdraw from school-based programs—alarmingly, the very same structures that could support them through PII. These behaviors are rooted in compounded vulnerability, that is, children’s overlapping experiences of parents’ imprisonment and precarious immigration status. Our study provides strong descriptive evidence of the extensive harms of PII for children. These results should prompt immediate action from policymakers who can legislate an end to incarceration in immigration legal proceedings. Our findings can also inform efforts by educators and schools to better support children experiencing PII.

Community violence-defined as unsanctioned violence between unrelated individuals in public places-has devastating physical, psychological, and emotional consequences on individuals, families, and communities. Immense investments in policing and incarceration in the United States have neither prevented community violence nor systemically served those who have been impacted by it, instead often inflicting further harm. However, the logics that uphold policing and incarceration as suitable or preventative responses to community violence are deeply ingrained in societal discourse, limiting our ability to respond differently. In this perspective, we draw from interviews with leading voices in the field of outreach-based community violence intervention and prevention to consider alternative ways to address community violence. We begin by demonstrating that policing and incarceration are distinguished by practices of retribution, isolation, and counterinsurgency that are counterproductive to the prevention of community violence. Then, we identify alternative practices of outreach-based community violence intervention and prevention that include (1) fostering safety nets through relationships among individuals, families, and neighborhoods, (2) fighting poverty and increasing access to resources, and (3) building political capacity among organizations to transform the broader systems in which they are embedded. They also include accountability practices that are preventative and responsive to the needs of those who are harmed. We conclude that elevating the language, narratives, and values of outreach-based community violence intervention and prevention can transform our responses to violence, interrupt cycles of harm, and foster safer communities.

This paper analyzes the intersection of individual lives and historical context by examining how cohort membership, historical conditions, and individual maturation influence subjective well-being in urban China. We use cross-classified multilevel models and repeated measures of happiness from seven waves of the Chinese General Social Survey (CGSS 2003–2013, N = 43,308). The results indicate that individuals born between 1956 and 1961 experienced setbacks at various pivotal moments throughout their life, including education, employment, economic stability, and social connections, and this cohort reports a lower overall sense of happiness when compared to other cohorts. The effect of aging on happiness comprises a U-shaped pattern; the middle-aged are the least happy. We observe an upward trend in happiness from 2003 to 2013. These results are confirmed by using subjective socioeconomic status (SES) as an alternative measure of well-being from CGSS 2003 and CGSS 2005 (N = 11,992). This paper contributes to studies of market transition by identifying the birth cohort as an important mechanism of inequality. It also augments the life-course paradigm by highlighting the significance of timing when individual lives intersect with historical context.