Social scientists routinely characterize religious influence in electoral politics as conservative and left-wing parties as fundamentally secular. Against these claims, I argue that the relationship between religion and electoral politics is shaped by the redistributive beliefs and preferences of religious leaders, who can become valuable allies of left-wing parties. I evaluate this argument in Brazil following the appointment of Pope John Paul II, leveraging as-if random variation in municipalities’ exposure to progressive Catholic bishops. I show that bishops who actively supported state-led redistribution were essential to the electoral success of the left-wing Workers’ Party (Partido dos Trabalhadores [PT]). Voters in municipalities with longer exposure to these bishops supported the PT at higher rates. The findings highlight the under-examined role of religious leaders in shaping the electoral influence of religion and provide evidence that these leaders can, in fact, be key for the development of left-wing parties, especially in the developing world.

We investigate the Stark operator restricted to a bounded domain $\Omega \subset \mathbb {R}^2$ with Dirichlet boundary conditions. In the semiclassical limit, a three-term asymptotic expansion for its individual eigenvalues has been established, with coefficients dependent on the curvature of $\Omega $. We analyse the accumulation of eigenvalues beneath the leading-order terms in these expansions, establishing Weyl-type asymptotics. Furthermore, we derive weak asymptotics for the density of the spectral projector onto these low-lying states.

This article focuses on how to answer the question: How can we better characterize the more significant epistemic advantages in knowledge-how of the members of oppressed social groups who are participating within scientific communities? My approach diverges from previous ways of answering this question by focusing on the epistemic advantages that are associated with the processes of critical reflection through which agents develop their conceptual frameworks. In particular, I argue that developing multiple, conflicting conceptual frameworks gives agents epistemic advantages in their abilities to both identify potentially problematic assumptions of these frameworks and to improve upon those assumptions that are judged to be problematic. That is to say, this paper aims to show both that there is a scientifically relevant knowledge-how of critically reflecting on the under-examined assumptions within one’s conceptual frameworks and that insider-outsider members of scientific communities will have certain epistemic advantages when it comes to developing this knowledge-how.

Evolutionary game-theoretic (EGT) models of morality face powerful under-addressed objections. Critics claim the simulations fail to specify their explanandum, muddying their explanatory value. Additionally, morality is suggested to be not computationally representable, jeopardising the method’s general applicability. This paper explicates and addresses the objections. I argue at least one concrete explication of morality, emotionism coupled with functionally understood emotions, can be a plausible subject of EGT explanations. I demonstrate how fixing this explanandum assuages the methodological objections and provide a computational model as proof of concept. If successful, the contribution placates serious long-standing criticisms of EGT as a meta-ethical tool.

Inter-party communication is crucial in representative democracies, facilitating information exchange and dialogue among political parties. Despite its importance, research on this topic remains limited due to lacking conceptual clarity and challenges in large-scale measurement. This article offers a comprehensive definition of inter-party communication as public communication by parties about others, with a positive, neutral, or negative stance, focusing on collaboration, policy, or personal issues. To effectively measure this phenomenon, we introduce a novel transformer-based approach capable of automatically classifying large volumes of text. Case studies on coalition signals in Germany and negative campaigning in Austria demonstrate its effectiveness. The study deepens our understanding of party competition, advances methods of automated text classification, and enables new research on political communication.

Maternal depressive symptoms during pregnancy have consequences for offspring brain development, likely mediated via biological signals. However, gestational biological correlates of maternal depression may differ depending on childhood maltreatment (CM) history. We investigated the association of maternal depressive symptoms in pregnancy and CM history with newborn global white matter microstructure. In a sample of N = 90 mother–infant dyads from two cohorts, maternal depressive symptoms were assessed with the Edinburgh Postnatal Depression Scale. CM was assessed with the Childhood Trauma Questionnaire or the Adverse Childhood Experiences scale. Diffusion-weighted imaging was performed in the infants within 90 days of birth. Fiber profiles of fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) were determined, and a global mean for each metric was computed. In adjusted models, there was a significant interaction effect of maternal depression and CM on newborn global FA (β = −0.523, p = .029) and RD (β = 0.590, p = .014) but not AD (β = 0.367, p = .120). In infants of women with CM history, maternal depressive symptoms were correlated negatively with FA and positively with RD. In contrast, infants of women without CM exhibited the reverse pattern of associations between depressive symptoms and diffusion metrics. These findings suggest that the impact of prenatal exposures, such as maternal depressive symptoms, on offspring brain development may be conditional on the presence or absence of maltreatment history. These findings highlight the importance of assessing trauma history and monitoring psychosocial well-being during pregnancy.

Generative soundscapes in exhibition spaces offer new possibilities for integrating artistic practice, technological innovation and perceptual experience. Contemporary tools – including stochastic algorithms, random oscillators and diverse methods of sound synthesis – enable the construction of environments that respond dynamically to external conditions. With the integration of artificial intelligence and machine learning, such systems acquire additional flexibility: they are able to register the presence and movement of visitors, evaluate changes in audience density and adjust to the acoustic properties of the space in real time. As a result, sound layers can emerge when a participant approaches, the balance of elements may shift with fluctuations in the crowd, and potential peaks in volume can be anticipated and mitigated. In this way, a fixed soundtrack is transformed into an adaptive system, where the exhibition environment functions as an active, responsive organism. Sound ceases to serve merely as a background and becomes a structural component that directly influences how the artistic work is perceived.

Marine heat waves (MHWs) are prolonged periods of elevated ocean temperatures that can devastate marine ecosystems, fisheries, and coastal communities. Skillfully predicting these events with sufficient lead time is crucial for mitigating their adverse effects. This study presents a probabilistic subseasonal MHW forecast tool using a U-Net-based neural network architecture, with a focus on the Northern Indian Ocean and the Arabian Sea. The model was trained using sea surface temperature and sea surface height reanalysis data. The U-Net-based forecast tool demonstrated significant predictive skill up to 10 weeks in advance across various deterministic and probabilistic skill metrics. The model outperformed persistence and climatology-based benchmarks, especially in the tropical warm pool. Future applications of explainable artificial intelligence (XAI) methods have the potential to identify the sources of predictive skill, inform understanding of underlying dynamics, and improve dynamic subseasonal to seasonal forecast models.

We consider a DA-type surgery of the famous Lorenz attractor in dimension 4. This kind of surgery was first used by Smale [Differentiable dynamical systems. Bull. Amer. Math. Soc. (N.S.) 73(6) (1967), 747–817] and Mañé [Contributions to the stability conjecture. Topology 17(4) (1978), 383–396] to give important examples in the study of partially hyperbolic systems. Our construction gives the first example of a singular chain recurrence class which is Lyapunov stable, away from homoclinic tangencies, and exhibits robustly heterodimensional cycles. Moreover, the chain recurrence class has the following interesting property: there exists robustly a two-dimensional sectionally expanding subbundle (containing the flow direction) of the tangent bundle such that it is properly included in a subbundle of the finest dominated splitting for the tangent flow.