A growing body of research in sociology uses the concept of cultural schemas to explain how culture influences beliefs and actions. However, this work often relies on belief or attitude measures gleaned from survey data as indicators of schemas, failing to measure the cognitive associations that constitute schemas. In this article, we propose a concept-association-based approach for collecting data about individuals’ schematic associations, and a corresponding method for modeling concept network representations of shared cultural schemas. We use this method to examine differences between liberal and conservative schemas of poverty in the United States, uncovering patterns of associations expected based on previous research. Examining the structure of schematic associations provides novel insights to long-standing empirical questions regarding partisan attitudes toward poverty. Our method yields a clearer picture of what poverty means for liberals and conservatives, revealing how different concepts related to poverty indeed mean fundamentally different things for these two groups. Finally, we show that differences in schema structure are predictive of individuals’ policy preferences.
Cultural sociologists frequently theorize about choices and decisions, although we tend to shy away from this language, and from concepts that are used by the judgment and decision-making (JDM) sciences. We show that cultural sociology and JDM are compatible and complementary fields by dispelling some common misunderstandings about JDM. We advocate for a strategic assimilation approach in which cultural sociologists are able to translate their work into key JDM terms like beliefs, preferences, and endowments. Learning to speak the JDM language will allow cultural sociologists to make important, and uniquely sociological, contributions to social scientific explanations of choices and decisions. KEYWORDS: theory; cognition; interdisciplinarity HIGHLIGHTS: • Cultural sociologists theorize about choices and decisions • Integrating with other fields who study these phenomena can prove fruitful • Learning the terminology used in these fields will facilitate this intergration
Symbolic valuation is an important but overlooked aspect of gendered processes of inequality in the occupation structure. Prior work has largely focused on the material valuation of gendered work, such as how much predominantly-female versus predominantly-male occupations pay. Less research has examined the symbolic valuation of work, such as how prestigious predominantly-female versus predominantly-male occupations are. What research has examined this question has remained inconclusive at best. Drawing on insights into and techniques from the sociology of culture and cognition, this study examines the role of an occupation’s gender composition in how Americans judge the prestige of jobs, testing key predictions from theories of gender and status. Using 2012 General Social Survey and federal occupation-level data, it finds evidence for a segregation premium: people view gender-segregated occupations as the most symbolically valuable jobs. Both men and women reward gender-segregated occupations with symbolic value, although there is evidence of a gendered in-group bias in which women in particular see women’s work as more prestigious, while men see men’s work as more prestigious.
Given the prestige and compensation of science and math-related occupations, the underrepresentation of women and people of color in science, technology, engineering, and mathematics majors (STEM) perpetuates entrenched economic and social inequities. Explanations for this underrepresentation have largely focused on individual characteristics, including uneven academic preparation, as well as institutional factors at the college level. In this article, we focus instead on high schools. We highlight the influence of the intersection between race and gender of female math and science teachers on students' decisions to major in STEM fields. Theoretically, this article extends the political science concept of representative bureaucracy to the issue of women's and disadvantaged minorities' underrepresentation in STEM majors. We analyze longitudinal data from public school students in North Carolina to test whether organizational demography of high school math and science faculty has an association with college major choice and graduation. Using hierarchical probit models with an instrumental-variable approach, we find that young white women are more likely to major in STEM fields and to graduate with STEM degrees when they come from high schools with higher proportions of female math and science teachers, irrespective of the race of the teacher. At the same time, these teachers do not depress young white or African American men's chances of majoring in STEM. Results for African American women are less conclusive, highlighting the limitations of their small sample size.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.