I checked 7 public opinion journals on Saturday, May 02, 2026 using the Crossref API. For the period April 25 to May 01, I found 5 new paper(s) in 3 journal(s).

Politics, Groups, and Identities

The summer of 2020: racialized framing and how threat is used to oppose social activism
J. Scott Carter, Annie Jones
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Showing up is the hardest part: examining geographical barriers to immigration court access
Miranda E. Sullivan
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Public Opinion Quarterly

Correcting Misperceptions Across Contexts: The Political Impact of Gender Inequality Information in Japan and South Korea
Min Hee Go, Yesola Kweon, Hirofumi Miwa, Yoshikuni Ono
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How do citizens respond to accurate information regarding social inequality? This study examines public perceptions of gender inequality in Japan and South Korea—two high-income democracies characterized by persistent gender gaps but contrasting political contexts. Using original survey data and preregistered survey experiments (N = 7,000), we test whether corrective information about gender disparities influences support for related government policies. The findings reveal notable cross-national variation: While corrective information had a minimal effect on Japanese respondents, it significantly increased support for gender equality measures among South Koreans, especially women. These varied findings suggest that the effectiveness of informational interventions depends on contextual factors. Specifically, we argue that the success of these interventions hinges on the political salience of the issue and the national informational environment, both of which enhance the likelihood of attitude change in the South Korean context. By examining misperceptions and their correction, this study contributes to the understanding of opinion formation, policy attitudes, and the conditional effects of information in shaping public perceptions of inequality.

Social Science Computer Review

A Bottom-Up Approach for Ecological Inference
Jose M. Pavía, Alberto Penadés
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The ecological inference canonical problem in the social sciences consists of estimating the unobserved internal counts of a global RxC table from the known margins of a set of units. This paper proposes a new, computation-based strategy designed to better exploit the information contained in the unit margins. This approach can be integrated into any ecological inference method that explicitly estimates unit tables and accounts for differences in unit size. We evaluate its performance using as a baseline the fastest ecological inference linear programming method, relying on real electoral data from over 550 datasets where true contingency tables are known. In this extensive assessment, the proposed strategy reduces average global errors by more than 21% relative to the baseline, outperforming it in 95% of cases. It also improves upon nslphom—identified in the literature as the most accurate algorithm for this dataset—reducing average global errors by over 5% and outperforming it in 60% of cases. The versatility of the approach is further illustrated by also integrating it into three more computationally intensive methods, including the two main statistical ecological inference models—ei.MD.bayes and BPF—and nslphom, yielding consistent improvements over their respective baselines in a small set of examples.
Expanding Your Vocabulary: A Framework for Topic Integration in Texts
Roy Gardner, Matthew Martin, Ashley Moran, Zachary Elkins, Andrés Cruz, Guillermo Pérez
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Topic discovery and integration are vital for maintaining vocabularies that categorize textual corpora. Automated approaches are often computationally expensive and lack domain-specific conceptual nuance; manual approaches are costly in terms of time and potential bias. To address this dilemma, we introduce the segments-as-topic (SAT) methodology, a four-stage process that combines automation and human expertise to assess candidate topics for vocabulary inclusion. In the SAT generation stage, a topic is formulated and refined through collaboration with domain experts, and then a sentence-level semantic similarity model retrieves corpus segments semantically aligned with the topic. The SAT expansion stage uses this seed set to find additional semantically similar segments, which are iteratively accepted or rejected to build a final segment set. During the review stage, a panel of scholars evaluates the topic for inclusion. In the integration stage, all segments in the final segment set are automatically tagged with the new topic. We apply this methodology to the Comparative Constitutions Project vocabulary that tracks over 330 topics in national constitutions, and demonstrate the addition of three new topics to the vocabulary. The SAT approach balances computational efficiency with expert judgment, offering a systematic, user-friendly, and replicable framework for social scientists to expand domain-specific vocabularies.