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1. Multisections with divides and Weinstein 4-manifolds

   https://doi.org/10.4310/jsg.240921005038  

   Islambouli, Gabriel; Starkston, Laura (January 2024, Journal of Symplectic Geometry)

   We introduce a new decomposition of Weinstein 4-manifolds called multisections with divides and show these can be encoded diagrammatically by a sequence of cut systems on a surface, together with a separating collection of curves. We give two algorithms to construct a multisection with divides for a Weinstein 4-manifold, one starting with a Kirby-Weinstein handle decomposition and the other starting with a positive, allowable Lefschetz fibration (PALF). Through the connections with PALFs, we define a monodromy of a multisection and show how to symplectically carry out monodromy substitution on multisections with divides. 

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2. Personal experiences bridge moral and political divides better than facts

   https://doi.org/10.1073/pnas.2008389118  

   Kubin, Emily; Puryear, Curtis; Schein, Chelsea; Gray, Kurt (January 2021, Proceedings of the National Academy of Sciences)

   Both liberals and conservatives believe that using facts in political discussions helps to foster mutual respect, but 15 studies—across multiple methodologies and issues—show that these beliefs are mistaken. Political opponents respect moral beliefs more when they are supported by personal experiences, not facts. The respect-inducing power of personal experiences is revealed by survey studies across various political topics, a field study of conversations about guns, an analysis of YouTube comments from abortion opinion videos, and an archival analysis of 137 interview transcripts from Fox News and CNN. The personal experiences most likely to encourage respect from opponents are issue-relevant and involve harm. Mediation analyses reveal that these harm-related personal experiences increase respect by increasing perceptions of rationality: everyone can appreciate that avoiding harm is rational, even in people who hold different beliefs about guns, taxes, immigration, and the environment. Studies show that people believe in the truth of both facts and personal experiences in nonmoral disagreement; however, in moral disagreements, subjective experiences seem truer (i.e., are doubted less) than objective facts. These results provide a concrete demonstration of how to bridge moral divides while also revealing how our intuitions can lead us astray. Stretching back to the Enlightenment, philosophers and scientists have privileged objective facts over experiences in the pursuit of truth. However, furnishing perceptions of truth within moral disagreements is better accomplished by sharing subjective experiences, not by providing facts. 

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3. Data Science Learning in Grades K–12: Synthesizing Research Across Divides

   https://doi.org/10.1162/99608f92.b1233596  

   Rosenberg, Joshua; Jones, Ryan Seth (April 2024, Harvard Data Science Review)

   What do we know about data science learning at the grades K–12 (precollegiate) level? This article answers this question by using the notion of agency to provide a framework to review the diverse research agendas and learning environments relevant to data science education. Examining research on data science education published in three recent special issues, we highlight key findings from scholars working in different communities using this lens. Then, we present the results of a co-citation coupling analysis for articles published in one of three recent data science education special issues with research spanning various levels and contexts. This co-citation analysis showed that while there are some common touchpoints, research on data science learning is taking place in a siloed manner. Based on our review of the literature through the lens of agency and our analysis, we discuss how the data science education community can synthesize research across disciplinary and grade-level divides. 

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4. Digital divides in scene recognition: uncovering socioeconomic biases in deep learning systems

   https://doi.org/10.1057/s41599-025-04719-w  

   Greene, Michelle R; Josyula, Mariam; Si, Wentao; Hart, Jennifer A (December 2025, Humanities and Social Sciences Communications)

   Automatic scene classification has applications ranging from urban planning to autonomous driving, yet little is known about how well these systems work across social differences. We investigate explicit and implicit biases in deep learning architectures, including deep convolutional neural networks (dCNNs) and multimodal large language models (MLLMs). We examined nearly one million images from user-submitted photographs and Airbnb listings from over 200 countries as well as all 3320 US counties. To isolate scene-specific biases, we ensured no people were in any of the photos. We found significant explicit socioeconomic biases across all models, including lower classification accuracy, higher classification uncertainty, and increased tendencies to assign labels that could be offensive when applied to homes (e.g., “slum”) in images from homes with lower socioeconomic status. We also found significant implicit biases, with pictures from lower socioeconomic conditions more aligned with word embeddings from negative concepts. All trends were consistent across countries and within the diverse economic and racial landscapes of the United States. This research thus demonstrates a novel bias in computer vision, emphasizing the need for more inclusive and representative training datasets. 

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5. In their God we trust: Religious cognition increases cooperation across religious divides.

   https://doi.org/10.1037/pspi0000499  

   Pasek, Michael H; Vishkin, Allon; Lehner, Anne; Shackleford, Crystal M; Hartley, Starlett; Ginges, Jeremy (July 2025, Journal of Personality and Social Psychology)