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Abstract Sociologists use the concept of narrative as an analytical tool and theoretical concept to understand the stories that people tell and their role in social and cultural life. A key tenet of prior research on narratives is their capacity to shape the audience’s understanding and evaluation of the narrator. In this mixed-method study, we investigate the role of narratives in destigmatization through the case of criminal record stigma in the labor market. Based on evidence from a survey experiment in which people with managerial experience were randomly assigned to job applicants with different narratives, we show that evaluations differ across reentry narratives. Drawing on prior theorizations and qualitative interviews with employers, we identify and describe three processes through which narratives impact evaluation and destigmatization: moral justification, social affinity signaling, and information salience. 

Modern computational text classification methods have brought social scientists tantalizingly close to the goal of unlocking vast insights buried in text data—from centuries of historical documents to streams of social media posts. Yet three barriers still stand in the way: the tedious labor of manual text annotation, the technical complexity that keeps these tools out of reach for many researchers, and, perhaps most critically, the challenge of bridging the gap between sophisticated algorithms and the deep theoretical understanding social scientists have already developed about human interactions, social structures, and institutions. To counter these limitations, we propose an approach to large-scale text analysis that requires substantially less human-labeled data, and no machine learning expertise, and efficiently integrates the social scientist into critical steps in the workflow. This approach, which allows the detection of statements in text, relies on large language models pre-trained for natural language inference, and a “few-shot” threshold-tuning algorithm rooted in active learning principles. We describe and showcase our approach by analyzing tweets collected during the 2020 U.S. presidential election campaign, and benchmark it against various computational approaches across three datasets. 

First-generation college students and those from ethnic groups such as African Americans, Latinx, Native Americans, or Indigenous Peoples in the United States are less likely to pursue STEM-related professions. How might we develop conceptual and methodological approaches to understand instructional differences between various undergraduate STEM programs that contribute to racial and social class disparities in psychological indicators of academic success such as learning orientations and engagement? Within social psychology, research has focused mainly on student-level mechanisms surrounding threat, motivation, and identity. A largely parallel literature in sociology, meanwhile, has taken a more institutional and critical approach to inequalities in STEM education, pointing to the macro level historical, cultural, and structural roots of those inequalities. In this paper, we bridge these two perspectives by focusing on critical faculty and peer instructor development as targets for inclusive STEM education. These practices, especially when deployed together, have the potential to disrupt the unseen but powerful historical forces that perpetuate STEM inequalities, while also positively affecting student-level proximate factors, especially for historically marginalized students.