The 2022 war in Ukraine has produced the biggest virtual humor archive in the history of wars. We argue that Ukrainian war humor is a form of civic activism in the name of Ukraine’s sovereignty. This civic activism is defined by resistance, solidarity, vigilance, and dedication to victory. The war humor circulates locally as well as on a global stage. It expresses the government’s positions and the people’s voices and empowers those affected by this war. Ukrainian war humor documents experiences of war realities; provides moral commentaries and emotional and aesthetic interpretations; and articulates visions for the future of Ukraine as a sovereign European state.

The aspirations-ability framework proposed by Carling has begun to place the question of who aspires to migrate at the center of migration research. In this article, building on key determinants assumed to impact individual migration decisions, we investigate their prediction accuracy when observed in the same dataset and in different mixed-migration contexts. In particular, we use a rigorous model selection approach and develop a machine learning algorithm to analyze two original cross-sectional face-to-face surveys conducted in Turkey and Lebanon among Syrian migrants and their respective host populations in early 2021. Studying similar nationalities in two hosting contexts with a distinct history of both immigration and emigration and large shares of assumed-to-be mobile populations, we illustrate that a) (im)mobility aspirations are hard to predict even under ‘ideal’ methodological circumstances, b) commonly referenced “migration drivers” fail to perform well in predicting migration aspirations in our study contexts, while c) aspects relating to social cohesion, political representation and hope play an important role that warrants more emphasis in future research and policymaking. Methodologically, we identify key challenges in quantitative research on predicting migration aspirations and propose a novel modeling approach to address these challenges.

There is growing concern about the impact of declining political trust on democracies. Psychological research has introduced the concept of epistemic (mis)trust as a stable disposition acquired through development, which may influence our sociopolitical engagement. Given trust’s prominence in current politics, we examined the relationship between epistemic trust and people’s choices of (un)trustworthy political leaders. In two representative samples in the UK and US (N = 1096), we tested whether epistemic trust predicts political leader choices through three political dimensions: dogmatism, political trust, and ideology. Although epistemic trust did not directly predict choices of political leaders, it predicted dogmatism and political ideology, which in turn predicted choices of political leaders. A network analysis revealed that epistemic trust and political dimensions only interact through their common connection with dogmatism. These findings suggest that cognitive and affective development may underlie an individual’s political ideology and associated beliefs.

Every complex organization is sometimes marked by preference heterogeneity, disagreement, and conflict. Within political parties, such frictions are traditionally viewed negatively, while recent research has started to perceive them more positively. How might such contradictory evaluations be explained? Through a three-step conceptual analysis we (1) identify two analytical perspectives on intraparty friction, one rooted in a primarily structural conception of parties, one in a primarily behavioral conception; and (2) specify a minimal definition of intraparty friction, which underpins a hierarchical concept structure to (3) suggest a way to resolve contradictions in the consequences attributed to intraparty frictions. Structuralist accounts often view frictions as negative due to a more demanding conceptual threshold, suggesting different types and levels of risk taking by conflict partners. Conversely, behavioralist perspectives see friction more often as beneficial because they focus on expressed disagreement without necessitating an organizational response. Our conceptual tools have important implications for research on membership organizations generally.

This technical note shows how we have combined prescriptive type checking and constraint solving to increase automation during software verification. We do so by defining a type system and implementing a typechecker for $\{log\}$ (read ‘setlog’), a Constraint Logic Programming language and satisfiability solver based on set theory. The constraint solver is proved to be safe w.r.t. the type system. Two industrial-strength case studies are presented where this combination is used with very good results.

What challenges do researchers encounter in authentically engaging with the field site and academia when certain aspects of their true identities diverge from the established norms within those domains? Using the case of female political scientists who conduct research on gender politics in the Middle East and North Africa, I highlight the ethical, logistical, and epistemological challenges of carrying out research in a politically and socially closed context. Few studies have investigated how the research process and the knowledge it produces are affected by the intertwinement of authoritarianism and patriarchy, and by the researcher’s positionality within this context. This study fills this gap by drawing upon interviews with feminist political scientists who were born and raised in the region but are based in Western academic institutions to examine the impact of authoritarianism, patriarchy, and the researchers’ insider/outsider positionality on the research process. The analysis shows three key findings. First, researching gender politics is a contentious topic that places researchers on the radar of the state. For scholars who are originally from the region, the issue is compounded by the fact that they are sometimes viewed as traitors by the regime in their country of origin, which accuses them of tarnishing the image of the government and scrutinizing its gender policies. Second, within the wider society, the politics of representation also impose certain limitations and expectations on female scholars. Such limitations include gendered restrictions on their access and mobility in the field. Finally, feminist researchers share how the knowledge they produce, which centers social justice demands, is not always valued in the discipline of political science. The article contributes to this discipline by expanding our understanding of the interplay between identity politics, fieldwork practices, and knowledge production in complex political and social settings.

Policy specialization in the U.S. Congress benefits the institution collectively and members individually. Yet members of Congress (MCs) are insufficiently specialized to optimize lawmaking success (Volden and Wiseman 2020). In this paper, we demonstrate the increasing propensity of MCs to generalize legislatively is driven largely by an expansion of MC legislative agendas in business domains. We then offer and test an explanation for this trend whereby business’s increasing demand for congressional attention (Drutman 2015) has outpaced the supply of congressional capacity to serve business needs (Crossen, Furnas, LaPira, and Burgat 2020; McKay 2022). This unmet demand incentivizes MCs to expand their business portfolio, which results in increased campaign contributions from business political action committees (PACs). We provide evidence consistent with this theory, showing that under conditions of access scarcity, MCs benefit financially (in terms of increased business PAC contributions) by broadening the number of business domains they are active in legislatively.

Introduction: the Scruton-Fidesz connection – Literature: Hungary’s rule of law backsliding and the Hungarian constitution – Methods: analysing ideological justifications and contemporary conservatism – Findings: the traditional family as the base for the existence of the nation – Individual rights’ dependency on communal obligations – Constitutional originalism and limitations on judicial review – History as a moral guide and historical revisionism – An exclusionary Hungarian constitutional identity – Analysis: traditionalist conservative values in the Hungarian Constitution and their manipulation – The radicalisation of conservatism – The struggle over the centre-right, and its implications on the future of democracy

A vast amount of clinical data are still stored in unstructured text. Automatic extraction of medical information from these data poses several challenges: high costs of clinical expertise, restricted computational resources, strict privacy regulations, and limited interpretability of model predictions. Recent domain adaptation and prompting methods using lightweight masked language models showed promising results with minimal training data and allow for application of well-established interpretability methods. We are first to present a systematic evaluation of advanced domain-adaptation and prompting methods in a lower-resource medical domain task, performing multi-class section classification on German doctor’s letters. We evaluate a variety of models, model sizes (further-pre)training and task settings, and conduct extensive class-wise evaluations supported by Shapley values to validate the quality of small-scale training data and to ensure interpretability of model predictions. We show that in few-shot learning scenarios, a lightweight, domain-adapted pretrained language model, prompted with just 20 shots per section class, outperforms a traditional classification model, by increasing accuracy from $48.6\%$ to $79.1\%$. By using Shapley values for model selection and training data optimization, we could further increase accuracy up to $84.3\%$. Our analyses reveal that pretraining of masked language models on general-language data is important to support successful domain-transfer to medical language, so that further-pretraining of general-language models on domain-specific documents can outperform models pretrained on domain-specific data only. Our evaluations show that applying prompting based on general-language pretrained masked language models combined with further-pretraining on medical-domain data achieves significant improvements in accuracy beyond traditional models with minimal training data. Further performance improvements and interpretability of results can be achieved, using interpretability methods such as Shapley values. Our findings highlight the feasibility of deploying powerful machine learning methods in clinical settings and can serve as a process-oriented guideline for lower-resource languages and domains such as clinical information extraction projects.