I checked 7 public opinion journals on Monday, January 26, 2026 using the Crossref API. For the period January 19 to January 25, I found 8 new paper(s) in 4 journal(s).

Journal of Official Statistics

A Decomposition of the Change in Total Poverty Gap
Andrea Marletta, Mauro Mussini
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The total poverty gap provides a straightforward information to policymakers as it measures the amount of income needed to get poor people out of poverty. Monitoring the change in total poverty gap is useful to examine poverty dynamics, however such a measure would be more informative if it were linked to other poverty indicators in a unified analysis framework. This paper suggests a decomposition that links the change in total poverty gap to those in poverty incidence, poverty depth, population size and composition. The decomposition is used to analyze the change in total poverty gap in Italy between 2010 and 2020. The total poverty gap decreased in the period considered and the change in poverty incidence was the main driver of such a reduction.

Journal of Survey Statistics and Methodology

PREDICTION OF POVERTY PROPORTIONS UNDER RANDOM REGRESSION COEFFICIENTS TWO-FOLD FAY-HERRIOT MODELS
Naomi Diz-Rosales, María José Lombardía, Domingo Morales
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Using a two-fold Fay-Herriot model with random intercepts and random regression coefficients, we develop area-level predictors for poverty proportions and propose both analytic and bootstrap-based estimators for the mean squared error. Model parameters are estimated via residual maximum likelihood, and empirical best linear unbiased predictors are used for random effects. To assess the performance of the estimation algorithm, predictors, and mean squared error estimators, we conduct simulation studies. The proposed methodology is applied to data from the 2022 Spanish Living Conditions Survey to estimate poverty proportions at the provincial level, disaggregated by gender and age group. This study offers a robust and innovative framework for small-area estimation, enabling detailed poverty mapping with accurate estimates and reliable uncertainty measures.

Public Opinion Quarterly

Not So Sexually Modern After All: Homonegativity and Prejudice Against Open and Age-Gap Relationships
Alberto LĂłpez Ortega, AgustĂ­n Blanco Bosco
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How widespread is sexual liberalism in tolerant societies? Theoretical and descriptive evidence suggests an overall liberalization in societal views on topics such as women’s rights and homosexuality. Yet, relying on sexual norms theory, this study unveils persistent sexual prejudice. We differentiate between “normalized” sexual issues, like same-gender marriage, which have gained mainstream acceptance, and “nonnormalized” issues, such as nontraditional sexual practices and relationships, which remain stigmatized. Through a conjoint experiment in Catalonia, Spain, we investigate public attitudes toward adoptive parents with varying sexual orientations, relationship types, and age differences, confirming that discriminatory preferences are prevalent in contexts with low social desirability. By highlighting the continued prejudice against both normative and nonnormative sexual issues, this research contributes to our understanding of the dynamics of sexual attitudes and the challenges facing LGBTQ+ politics and rights.

Social Science Computer Review

Capability, Opportunity, and Motivation in a Social Multiplayer Online Game: Player Influence Dynamics in Sky: Children of Light
Wen Zeng, Chandni Kumar, Sinong Zhou, Donggyu Kim, Aimei Yang, Dmitri Williams
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This study investigates networked social influence in Sky: Children of the Light , a social multiplayer online game. Drawing on survey responses ( n = 9,254) and in-game data from over 660,000 players, we use an innovative graph-based machine learning approach to quantify how individuals influence others’ playtime, and regression analyses to test predictors from the COM-B model. Results show that Capability enhances influence, although excessive task focus correlates negatively with social impact; Opportunity emerges as the strongest predictor, with active social interactions significantly boosting influence; and Motivation varies by playstyle, with socializers and competitors demonstrating greater influence than narrative-focused players. By applying the COM-B model in a digital gaming context, this research highlights behavioral dimensions of player influence and employs a novel metric for quantifying interpersonal influence. These findings suggest practical implications for game design, particularly by highlighting how social interaction opportunities and different player motivations shape influence within communities.
Global Gender Inequality Through Explainable AI: Machine Learning, Clustering, and SHAP Insights
Sadullah Çelik, Cemile Zehra Köroğlu
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Objective: This paper analyzes gender equality across countries in the year 2024 by using the GGGI, with the intention of disentangling the unseen structural and non-deterministic patterns. Instead of repeating the process of calculating the index, it is openly recognizing the compositional feature of the GGGI and the unseen similarities between the indices. Methods: This research employs a global cross-sectional study of 146 countries over the four primary GGGI sectors: economic participation, education, health and survival, and empowerment. Where OLS is only employed as a diagnostic test, as its almost perfect fit (R 2 ∌1) is squarely mechanical and lacks relevance for inference. Apart from ensemble models employed for predictions, K-means clustering, SHAP analysis, and GridSearchCV optimization are also used. Findings: The out-of-sample predictions demonstrate high levels of predictive accuracy, with Gradient Boosting models yielding an R 2 of approximately 0.90 and an RMSE of approximately 0.045, indicating that there is significant nonlinear information beyond index aggregation. Unsupervised clustering techniques show that there are seven distinct country clusters that go beyond traditional geographic and income divisions, which can be identified with more than 93% accuracy. The SHAP results show that empowerment and economic participation are drivers, while there is insignificant variation in healthcare. Contribution: This study identifies the boundaries of regression analysis in index research, as well as the advantages of machine learning analysis in determining structural patterns related to gender equity.
From Search to Separation: Digital Behavioral Decoupling and the Predictive Power of Google Trends for Divorce Outcomes Across Four Western Nations
Emre Can Kuran, Umut Kuran
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Digital search platforms enable real-time observation of relationship distress through behavioral traces. This study tests whether Google Trends predicts official divorce rates in the United States, Germany, the Netherlands, and the United Kingdom from 2009 to 2023. We introduce Digital Behavioral Decoupling, in which online distress signals diverge from legal outcomes as divorce shifts from an institutional procedure to an emotionally mediated digital phenomenon. Methods include unit root tests, cointegration analysis, Granger causality, spectral coherence, and rolling-origin nowcasting. Search queries are grouped into pre-divorce (cognitive distress), during-divorce (procedural action), and post-divorce (emotional recovery) phases. Results show 62.5% of terms lead divorce rates by 1–2 years, yet only 8.3% remain significant after False Discovery Rate correction. The Netherlands demonstrates 100% forecast improvements beyond autoregressive models (DM 2.87–3.43, p < 0.02) across all search terms, from early relationship therapy queries through procedural and post-divorce searches, indicating systematic capture of the entire divorce pathway. Germany shows intermediate results with 33% forecast success beyond autoregressive benchmarks (DM 2.40–2.81) limited to problem-recognition terms, suggesting episodic crisis-driven engagement. The United States and United Kingdom show no forecast gains beyond autoregressive models despite high search volumes, consistent with information saturation in normalized divorce cultures. Lead-lag relationships are frequency-specific, concentrated at 3–5 year periodicities. Findings link family sociology with affective computing and provide a replicable toolkit for tracking relationship dissolution in algorithmically curated information environments.
Avatar Anthropomorphism and Metaverse Marketing Adoption: A Social Cognitive Perspective
Aasir Ali, Yasir Hussain, Umair Tufail
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This study investigates the impact of self-efficacy among marketing professionals with digital tools affecting their strategic flexibility (ability to adjust marketing strategies to changing trends) and career adaptability (willingness to learn or change roles), and how these variables influence the adoption of metaverse marketing tools. Data were gathered using a cross-sectional online survey of 395 marketing and digital strategy professionals working in the e-commerce, digital marketing, information technology, and educational-technology sectors in China. Structural Equation Modeling (SEM) was used to examine a moderated mediation model between self-efficacy, strategic flexibility, career adaptability, and metaverse marketing adoption. Results show that Marketing self-efficacy significantly predicts strategic flexibility (ÎČ = 0.35, p < 0.001) and career adaptability (ÎČ = 0.30, p < 0.001) which fully mediate its impact on adoption, indicating an indirect behavioral path. Interestingly, avatar anthropomorphism strengthens these relationships (ÎČ = 0.20, p < 0.01 for strategic flexibility; ÎČ = 0.18, p < 0.01 for career adaptability) with higher human-likeness intensifying the mediated effects and highlighting the impact of anthropomorphic cues in virtual environments. This study provides insights for equipping professionals for digital transformation and offering actionable strategies for designing avatars to improve technology adoption in virtual marketing environments. The findings highlight that well calibrating avatar features—responsiveness, realism, and feedback—can enhance perceived trust and usability and social presence. Overall, this study extends research on virtual environments by identifying avatar anthropomorphism as a key boundary condition in technology adoption and offering design implications for AI-driven metaverse interfaces.
Procedural Rhetorics Meet Platform Affordances: An Exploration of Community Rhetoric on Reddit
Mariacristina Sciannamblo, Enrico Gandolfi
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This article investigates the rhetorical dynamics emerging within Reddit communities, focusing on discussions surrounding the video game franchise The Last of Us . Building on the concept of procedural rhetoric, the paper suggests framing the collective articulation of meaning that emerges from the interaction between digital affordances—such as upvoting, moderation, and subreddit structures—and community practices in terms of “community rhetoric.” Drawing from a thematic analysis of posts and comments in two dedicated subreddits, the article identifies how content, attitude, and discursive climate shape participatory discourse. This framework is enriched by considering Reddit’s distinctive technical configurations, including pseudonymity and community self-governance, which foster specific discursive cultures. Rather than presenting Reddit as a neutral container, the study highlights how its infrastructural features actively mediate rhetorical production and sense-making. The paper contributes to digital rhetoric and media studies by offering a model that integrates platform affordances with user-driven cultural practices, showing how community engagement shapes knowledge, authority, and cultural narratives in online spaces. Limitations and future directions are discussed in light of applying the framework to other media fandoms and social platforms.