I checked 7 public opinion journals on Monday, May 25, 2026 using the Crossref API. For the period May 18 to May 24, I found 6 new paper(s) in 4 journal(s).

Journal of Elections, Public Opinion and Parties

Popular support for election law reforms: the effects of political self-interest and civic values
Daniel Montez, William Mishler
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Politics, Groups, and Identities

Intersectional and historical contingencies in racial capitalism: credit scores and predatory inclusion in American political economy
Tess Wise
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The political nature of union locals: a survey of Ohio labor unions
Kevin Reuning
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Political parenthood: gendered expectations and voter responses to work–family balance
Asha Venugopalan
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Public Opinion Quarterly

The Social Nature of Political (Dis)Interest: Conceptualizing and Validating Political (Dis)Interest as a Social Identity
Céline M Laffineur, Bert N Bakker, Gijs Schumacher
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Political interest is fundamental to democratic engagement. Yet, its conceptualization remains debated. In this agenda-setting study, we offer a new but important perspective by conceptualizing political (dis)interest as a social identity. We introduce two innovative measures: the Positive Political Interest Identity Scale (PPIS) and the Negative Political Interest Identity Scale (NPIS). Employing Item Response Theory, we demonstrate the construct validity of both scales in a preregistered study in the Netherlands (N = 1,553). Using the same dataset, we demonstrate their predictive validity. Specifically, the PPIS and NPIS uncover substantial differences in political attitudes and behaviors both between and within individuals who identify as politically (dis)interested. Conceptualizing political (dis)interest as a social identity enriches our understanding of the concept and its implications for politically relevant attitudes and behaviors, while also informing interventions to foster political interest equally across all citizens.

Social Science Computer Review

Can Machines Perform a Qualitative Data Analysis? Reading the Debate With Alan Turing
Stefano De Paoli
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This paper reflects on the literature that rejects the use of Large Language Models (LLMs) in qualitative data analysis (QDA). It illustrates through empirical evidence and critical reflections why the current debate is focusing on the wrong problems. The paper proposes that a key focus of researching the use of the LLMs for QDA is the empirical investigation of a hybrid/artificial system performing an analysis. The paper builds on the seminal work of Alan Turing and reads the current debate using key ideas from Turing’s “Computing Machinery and Intelligence.” This paper reframes the debate on QDA with LLMs and states that rather than asking whether machines can perform qualitative analysis in principle, we should ask whether with LLMs and researchers together can produce analyses that are sufficiently comparable to human analysts alone. In the final part the contrary views to performing QDA with LLMs are analysed using the same writing and rhetorical style that Turing used in his seminal work.