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This article uses network and text analyses methods to reexamine the intersections of race, class, gender, and ethnicity in first-wave feminist organizations in Chicago during the Progressive Era, from 1860 to 1920.

Narrating history is perpetually contested, shaping and reshaping how nations and people understand both their pasts and the current moment. Measuring and evaluating the scope of histories is methodologically challenging. In this paper we provide a general approach and a specific method to measure historical recall.

I empirically demonstrate the alignment between machine learning and inductive research through a word embedding model of first-person narratives of the nineteenth-century U.S. South.

Using a combination of network analysis, computational text analysis, and qualitative interpretation, the author examines women’s movement discourses across the first and second waves of activism and between two sites, New York City and Chicago. Place, she finds, serves to capture differences in political logics that generate durable differences within movement discourse.

Using an inductive, theory-building approach and combination of computational and qualitative methods, we investigate how the concept of implicit bias was translated into practice through the ADVANCE program and identify five key features that made implicit bias useful as a change framework in the academic STEM setting.

This article compares three common computer-assisted approaches—dictionary, supervised machine learning, and unsupervised machine learning—to those produced through a rigorous hand-coding analysis of inequality in the news.

Using the U.S. environmental movement as a case study, and employing a data-driven and inductive strategy that combines both computational and qualitative methods, we find that strategy emerges as organizations link their actions to their goal orientation: what level of society the organization views as the locus of change.

This article proposes a three-step methodological framework called computational grounded theory, which combines expert human knowledge and hermeneutic skills with the processing power and pattern recognition of computers, producing a more methodologically rigorous but interpretive approach to content analysis.

In the lead up to International Women’s Day on Friday March 8th, SAGE Ocean posed a series of questions to leading academics. In this post I explores how we nurture an academic landscape that is more accessible to women.

In this blog post I explore a fundamental question in computational social science: does “big computation” necessarily comes at the expense of narrative richness, contextual detail, and theory? Or, conversely, narrative richness can only happen without computation?