Previous scholarship has demonstrated the value of high-impact practices of community engagement, inquiry-based pedagogy, and collaborative learning for engagement and learning in sociology courses, especially undergraduate research methods and statistics. This article explores the changes made to an upper-division undergraduate course focused on applied research practices and community-level interventions.
This article investigates the effects of teaching about metacognition in a sociological theory course. I created a series of teaching interventions to introduce students to the science of learning, including an interactive lecture on metacognition, a discussion that models metacognitive strategies, and activities for students to practice metacognition. This article describes those teaching interventions and assesses whether direct instruction led to greater use of metacognitive and cognitive strategies, confidence, and motivation to learn.
We discuss findings from a survey of sociology students in Germany and consequences for teaching. We focus on the de facto formation of a sociological canon, the relation between theories and methods, and effects of social and political characteristics on student’s scientific preferences. Our findings suggest that irrespective of an agreement of the sociological professionals on a common definition of a core, a de facto canon of theories and methods exists in teaching practices. Moreover, specific relations between sociological theories and methods occur in the data.
We provide an overview of associations between income inequality and intergenerational mobility in the United States, Canada, and eight European countries. We analyze whether this correlation is observed across and within countries over time. We investigate Great Gatsby curves and perform metaregression analyses based on several papers on this topic. Results suggest that countries with high levels of inequality tend to have lower levels of mobility.
“All that is solid melts into air,” wrote Marx and Engels in The Communist Manifesto, at a time when labor was becoming increasingly precarious. The experience of workplace precarity and the broader feeling of insecurity it engenders are certainly not new; they are as old as capitalism. Even so, precarious labor as a concept is enjoying quite a boom these days.
ASA speaks with sociology graduate student Simone Kolysh at the 2016 ASA Annual Meeting on August, 2016, in Seattle, WA. Kolysh talks about what it means to “do sociology,” how she uses sociology in her work, highlights of her work in the field, the relevance of sociological work to society, and her advice to students interested in entering the field.
Racial stratification is well documented in many spheres of social life. Much stratification research assumes that implicit or explicit bias on the part of institutional gatekeepers produces disparate racial outcomes. Research on status-based expectations provides a good starting point for theoretically understanding racial inequalities. In this context it is understood that race results in differential expectations for performance, producing disparate outcomes.
Most intergenerational mobility studies rely on either snapshot or time-averaged measures of earnings, but have yet to examine resemblance of earnings trajectories over the life course of successive generations. We propose a linked trajectory mobility approach that decomposes the progression of economic status over two generations into associations in four life-cycle dimensions: initial position, growth rate, growth deceleration, and volatility.
We study the relationship between inter-class inequality and intergenerational class mobility across 39 countries. Previous research on the relationship between economic inequality and class mobility remains inconclusive, as studies have confounded intra- with between-class economic inequalities. We propose that between-class inequality across multiple dimensions accounts for the inverse relationship between inequality and mobility: the larger the resource distance between classes, the less likely it is that mobility from one to the other will occur.
Corporations gather massive amounts of personal data to predict how individuals will behave so that they can profitably price goods and allocate resources. This article investigates the moral foundations of such increasingly prevalent market practices. I leverage the case of credit scores in car insurance pricing—an early and controversial use of algorithmic prediction in the U.S. consumer economy—to unpack the premise that predictive data are fair to use and to understand the conditions under which people are likely to challenge that moral logic.