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Identity theory (IT) and social identity theory (SIT) are eminent research programs from sociology and psychology, respectively. We test collective identity as a point of convergence between the two programs. Collective identity is a subtheory of SIT that pertains to activist identification. Collective identity maps closely onto identity theory’s group/social identity, which refers to identification with socially situated identity categories. We propose conceptualizing collective identity as a type of group/social identity, integrating activist collectives into the identity theory model.
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Protest event analysis is an important method for the study of collective action and social movements and typically draws on traditional media reports as the data source. We introduce collective action from social media (CASM)—a system that uses convolutional neural networks on image data and recurrent neural networks with long short-term memory on text data in a two-stage classifier to identify social media posts about offline collective action. We implement CASM on Chinese social media data and identify more than 100,000 collective action events from 2010 to 2017 (CASM-China).
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Ideologies that support racial domination and White supremacy remain foundational in U.S. society, even as the nation becomes increasingly diverse and progressively focused on quantitative measurement. This study explores how a prominent mainstream news outlet represents the growth of the nation’s second largest population, Latinos, within this changing demographic and numeric environment.
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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.
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“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.
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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.
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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.
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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.
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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.
