This Trends article reviews recent scholarship (2020-2024) on the relationship between economic inequality and political participation. For decades, research has been guided by two dominant theories: Conflict Theory (CT), which posits that inequality stimulates political engagement, and Relative Power Theory (RPT), which conversely predicts that inequality depresses participation. Despite their prominence, empirical studies seeking to adjudicate between both theories have produced contradictory results. Synthesizing the recent literature, this article argues that such inconsistencies have prompted a shift away from assessing whether inequality uniformly increases (CT) or decreases (RPT) political participation. Instead, contemporary scholarship has turned to investigating the conditions under which inequality mobilizes or demobilizes citizens. Three major trends drive this reorientation: (1) the expansion of economic inequality measures beyond national-level income indicators to include finer-grained and subjective operationalizations; (2) the broadening of the concept of political participation beyond voter turnout to encompass diverse participatory behaviors; and (3) the growing examination of mediating and moderating variables that condition the inequalityparticipation relationship. Collectively, this article argues that these developments demonstrate that RPT and CT are best conceptualized as conditional rather than universal explanatory frameworks.
Lorcan McLaren, James Cross, Zuzanna Krakowska
et al.
Political scientists are rapidly adopting large language models (LLMs) for text annotation, yet the sensitivity of annotation results to implementation choices remains poorly understood. Most evaluations test a single model or configuration; how model choice, model size, learning approach, and prompt style interact, and whether popular "best practices" survive controlled comparison, are largely unexplored. We present a controlled evaluation of these pipeline choices, testing six open-weight models across four political science annotation tasks under identical quantisation, hardware, and prompt-template conditions. Our central finding is methodological: interaction effects dominate main effects, so seemingly reasonable pipeline choices can become consequential researcher degrees of freedom. No single model, prompt style, or learning approach is uniformly superior, and the best-performing model varies across tasks. Two corollaries follow. First, model size is an unreliable guide both to cost and to performance: cross-family efficiency differences are so large that some larger models are less resource-intensive than much smaller alternatives, while within model families mid-range variants often match or exceed larger counterparts. Second, widely recommended prompt engineering techniques yield inconsistent and sometimes negative effects on annotation performance. We use these benchmark results to develop a validation-first framework - with a principled ordering of pipeline decisions, guidance on prompt freezing and held-out evaluation, reporting standards, and open-source tools - to help researchers navigate this decision space transparently.
YouTube has today become the primary news source for many users, which raises concerns about the role its recommendation algorithm can play in the spread of misinformation and political polarization. Prior work in this area has mainly analyzed how recommendations evolve based on users' watch history within the platform. Nevertheless, recommendations can also depend on off-platform browsing activity that Google collects via trackers on news websites, a factor that has not been considered so far. To fill this gap, we propose a sock-puppet-based experimental framework that automatically interacts with news media articles and then collects YouTube recommendations to measure how cross-site tracking affects the political and misinformation content users see. Moreover, by running our audits in both tracking-permissive and tracking-restrictive browser environments, we assess whether common privacy-focused browsers can protect users from tracking-driven political and misinformation bubbles on YouTube.
Cristine Koehler Zanella, Edson José Neves Junior, Lívia Ribeiro da Silva
Ao longo do século 20, aspectos culturais passaram a ser mais mobilizados como parte da estratégia diplomática dos Estados. A internacionalização do ensino, especialmente a mobilidade acadêmica internacional, foi um dos recursos utilizados para promover o interesse nacional, influenciar a percepção de estrangeiros e favorecer o entendimento mútuo com outras nações. O presente artigo analisa o programa Global Korea Scholarship (GKS) como instrumento de diplomacia cultural sul-coreana. A partir de revisão bibliográfica e análise documental de políticas oficiais sul-coreanas, argumenta-se que o GKS opera como prática estruturada de diplomacia cultural, contribuindo para o fortalecimento do conhecimento sobre a Coréia do Sul e para o reforço do prestígio do país no sistema internacional, além de agir também numa dimensão mais simbólica e interpessoal. O estudo contribui para ampliar o debate sobre o papel da cultura nas Relações Internacionais a partir da experiência de países não ocidentais no campo da diplomacia cultural.
International relations, Social sciences (General)
The European integration process Currently, under the conditions of the new geopolitical realities present on the European continent, conditioned in particular by the emergence on February 24, 2022 of the Russian-Ukrainian war, it has contributed to the acceleration of the European Union's relations with the states of the Balkans, including also with the states of Eastern Europe - Ukraine and the Republic of Moldova. In the last decades, the process of European integration has become a subject of increased interest. Nine states from this area have expressed their desire to become members of the European Union. In order to align with European standards and values, the candidate states for joining the European integrationist space are making efforts to implement reforms in various areas, including electoral legislation, the judicial system, the fight against corruption and organized crime, as well as the improvement of detention conditions. However, the process of European integration is not without challenges and obstacles, and some states have made slower progress than others.
Thus, the primary objective of this article is to analyze the current situation of the states that hold the status of candidate for joining the EU space, including highlighting the degree of preparation of the states for joining the European integrationist space, as well as highlighting the problems faced by the candidate states for joining.
We explore how large language models (LLMs) assess offensiveness in political discourse when prompted to adopt specific political and cultural perspectives. Using a multilingual subset of the MD-Agreement dataset centered on tweets from the 2020 US elections, we evaluate several recent LLMs - including DeepSeek-R1, o4-mini, GPT-4.1-mini, Qwen3, Gemma, and Mistral - tasked with judging tweets as offensive or non-offensive from the viewpoints of varied political personas (far-right, conservative, centrist, progressive) across English, Polish, and Russian contexts. Our results show that larger models with explicit reasoning abilities (e.g., DeepSeek-R1, o4-mini) are more consistent and sensitive to ideological and cultural variation, while smaller models often fail to capture subtle distinctions. We find that reasoning capabilities significantly improve both the personalization and interpretability of offensiveness judgments, suggesting that such mechanisms are key to adapting LLMs for nuanced sociopolitical text classification across languages and ideologies.
Vivek Sharma, Mohammad Mahdi Shokri, Sarah Ita Levitan
et al.
News outlets are well known to have political associations, and many national outlets cultivate political biases to cater to different audiences. Journalists working for these news outlets have a big impact on the stories they cover. In this work, we present a methodology to analyze the role of journalists, affiliated with popular news outlets, in propagating their bias using some form of propaganda-like language. We introduce JMBX(Journalist Media Bias on X), a systematically collected and annotated dataset of 1874 tweets from Twitter (now known as X). These tweets are authored by popular journalists from 10 news outlets whose political biases range from extreme left to extreme right. We extract several insights from the data and conclude that journalists who are affiliated with outlets with extreme biases are more likely to use propaganda-like language in their writings compared to those who are affiliated with outlets with mild political leans. We compare eight different Large Language Models (LLM) by OpenAI and Google. We find that LLMs generally performs better when detecting propaganda in social media and news article compared to BERT-based model which is fine-tuned for propaganda detection. While the performance improvements of using large language models (LLMs) are significant, they come at a notable monetary and environmental cost. This study provides an analysis of both the financial costs, based on token usage, and the environmental impact, utilizing tools that estimate carbon emissions associated with LLM operations.
This study examines the structural dynamics of Truth Social, a politically aligned social media platform, during two major political events: the U.S. Supreme Court's overturning of Roe v. Wade and the FBI's search of Mar-a-Lago. Using a large-scale dataset of user interactions based on re-truths (platform-native reposts), we analyze how the network evolves in relation to fragmentation, polarization, and user influence. Our findings reveal a segmented and ideologically homogenous structure dominated by a small number of central figures. Political events prompt temporary consolidation around shared narratives, followed by rapid returns to fragmented, echo-chambered clusters. Centrality metrics highlight the disproportionate role of key influencers, particularly @realDonaldTrump, in shaping visibility and directing discourse. These results contribute to research on alternative platforms, political communication, and online network behavior, demonstrating how infrastructure and community dynamics together reinforce ideological boundaries and limit cross-cutting engagement.
This study examines how political engagement shapes public attitudes toward legal immigration in the United States. Using nationally weighted data from the 2024 ANES Pilot Study, we construct a novel Political Engagement Index (PAX) based on five civic actions: discussing politics, online sharing, attending rallies, wearing political symbols, and campaign volunteering. By applying weighted ordered logistic regression models, we find that higher engagement predicts greater support for easing legal immigration, even after adjusting for education, gender, age, partisanship, income, urban residence, and generalized social trust. To capture the substantive effect, we visualize predicted probabilities across levels of engagement. In full-sample models, the likelihood of supporting "a lot harder" immigration drops from 26% to 13% as engagement rises, while support for "a lot easier" increases from 10% to 21%. Subgroup analyses by partisanship show consistent directionality, with notable shifts among Republicans. Social trust and education are also consistently associated with more open attitudes, while older respondents tend to support less lenient pathways to legal immigration policies. These findings suggest that a cumulative increase in political participation is linked to support for legal immigration pathways, with varying intensity across partisan identities and socio-demographic characteristics.
This paper investigates the usage patterns of Facebook among different demographics in the United States, focusing on the consumption of political information and its variability across age, gender, and ethnicity. Employing a novel data donation model, we developed a tool that allows users to voluntarily share their interactions with public Facebook groups and pages, which we subsequently enrich using CrowdTangle. This approach enabled the collection and analysis of a dataset comprising over 1,200 American users. Our findings indicate that political content consumption on Facebook is relatively low, averaging around 17%, and exhibits significant demographic variations. Additionally, we provide insights into the temporal trends of these interactions. The main contributions of this research include a methodological framework for studying social media usage in a privacy-preserving manner, a comprehensive dataset reflective of current engagement patterns, and descriptive insights that highlight demographic disparities and trends over time. This study enhances our understanding of social media's role in information dissemination and its implications for political engagement, offering a valuable resource for researchers and policymakers in a landscape where direct data access is diminishing.
Astronomical solutions provide calculated orbital and rotational parameters of solar system bodies based on the dynamics and physics of the solar system. Application of astronomical solutions in the Earth sciences has revolutionized our understanding in at least two areas of active research. (i) The Astronomical (or Milankovic) forcing of climate on time scales > ~10 kyr and (ii) the dating of geologic archives. The latter has permitted the development of the astronomical time scale, widely used today to reconstruct highly accurate geological dates and chronologies. The tasks of computing vs. applying astronomical solutions are usually performed by investigators from different backgrounds, which has led to confusion and recent inaccurate results on the side of the applications. Here we review astronomical solutions and Milankovic forcing in the Earth sciences, primarily aiming at clarifying the astronomical basis, applicability, and limitations of the solutions. We provide a summary of current up-to-date and outdated astronomical solutions and their valid time span. We discuss the fundamental limits imposed by dynamical solar system chaos on astronomical calculations and geological/astrochronological applications. We illustrate basic features of chaotic behavior using a simple mechanical system, i.e., the driven pendulum. Regarding so-called astronomical "metronomes", we point out that the current evidence does not support the notion of generally stable and prominent metronomes for universal use in astrochronology and cyclostratigraphy. We also describe amplitude and frequency modulation of astronomical forcing signals and the relation to their expression in cyclostratigraphic sequences. Furthermore, the various quantities and terminology associated with Earth's axial precession are discussed in detail. Finally, we provide some suggestions regarding practical considerations.
Sebastien Bourdin, Leila Kebir, Stanislav Ivanov
et al.
In recent years, the tourism sector has undergone a significant transformation, driven by the rise of technology and digitalization. Whereas travel was once seen as an escape from everyday life, it is now augmented and enriched by a wide range of digital tools that shape every stage of the traveler's experience (Ahmad et al., 2023). From this point of view, the pandemic has acted as a driving force behind this digitalization, both in terms of services offered by tourism actors and the openness of tourists toward digital tools, smart apps, and immersive experiences. Digitalisation has also served as a catalyst for numerous strategies aimed at enhancing the territorial resilience of the European Union in response to this unforeseen shock (Entin & Galushko, 2021).
Public opinion is a crucial factor in shaping political decision-making. Nowadays, social media has become an essential platform for individuals to engage in political discussions and express their political views, presenting researchers with an invaluable resource for analyzing public opinion. In this paper, we focus on the 2020 US presidential election and create a large-scale dataset from Twitter. To detect political opinions in tweets, we build a user-tweet bipartite graph based on users' posting and retweeting behaviors and convert the task into a Graph Neural Network (GNN)-based node classification problem. Then, we introduce a novel skip aggregation mechanism that makes tweet nodes aggregate information from second-order neighbors, which are also tweet nodes due to the graph's bipartite nature, effectively leveraging user behavioral information. The experimental results show that our proposed model significantly outperforms several competitive baselines. Further analyses demonstrate the significance of user behavioral information and the effectiveness of skip aggregation.
Data science is not a science. It is a research paradigm with an unfathomed scope, scale, complexity, and power for knowledge discovery that is not otherwise possible and can be beyond human reasoning. It is changing our world practically and profoundly already widely deployed in tens of thousands of applications in every discipline in an AI Arms Race that, due to its inscrutability, can lead to unfathomed risks. This paper presents an axiology of data science, its purpose, nature, importance, risks, and value for problem solving, by exploring and evaluating its remarkable, definitive features. As data science is in its infancy, this initial, speculative axiology is intended to aid in understanding and defining data science to recognize its potential benefits, risks, and open research challenges. AI based data science is inherently about uncertainty that may be more realistic than our preference for the certainty of science. Data science will have impacts far beyond knowledge discovery and will take us into new ways of understanding the world.
In this paper, using Monte Carlo simulations we show that the Blume-Capel model gives rise to the social depolarization. This model borrowed from statistical physics uses the continuous Ising spin varying from -1 to 1 passing by zero to express the political stance of an individual going from ultra-left (-1) to ultra-right (+1). The particularity of the Blume-Capel model is the existence of a $D$-term which favors the state of spin zero which is a neutral stance. We consider the political system of the USA where voters affiliate with two political groups: Democrats or Republicans, or are independent. Each group is composed of a large number of interacting members of the same stance. We represent the general political ambiance (or degree of social turmoil) with a temperature $T$ similar to thermal agitation in statistical physics. When three groups interact with each other, their stances can get closer or further from each other, depending on the nature of their inter-group interactions. We study the dynamics of such variations as functions of the value of the $D$-term of each group. We show that the polarization decreases with increasing $D$. We outline the important role of $T$ in these dynamics. These MC results are in excellent agreement with the mean-field treatment of the same model.
The article deals with the market knowledge and the activities of educational institutions, the conclusion about the necessity of reforming the system of high school based on the concept of open education is determined by the economic impact of the use of distance learning network technology as an index of cost reducing costs, the price is determined by the knowledge of a specialist trained on the new non-standard shape training.
The article considers the issues of professional training of specialists in the field of public administration. The specifics of public administration are studied. It is determined that the purpose of professional training of specialists in the field of public administration is the formation and development of a new generation of specialists in public administration. The provisions of the standard of higher education in the specialty 281 «Public Administration» for the first (bachelor’s) and second (master’s) level of higher education are studied. It is established that in order to increase the efficiency and quality of professional training of specialists in the field of public administration and administration, it is expedient to orient students to choose the specialization that will best meet their expectations regarding future employment. The importance of competencies in ensuring the successful work of future specialists in the field of public administration is justified.The American experience of training specialists in the field of public administration, in particular on the formation of professional competencies, is studied. It was found that in American educational institutions, along with the main disciplines and elective courses that students choose depending on their future specialization, in-depth courses are taught in the most relevant areas (spheres) in public policy-making.The program results of training of future specialists in the field of public administration are given. It is substantiated that the experience of the school of public administration in the United States is based on the introduction of modern trends in public administration, styles of administration, creative thinking, new information technology capabilities, rethinking the requirements for professional and personal qualities of public servants.It is expedient to improve the standard of higher specialty 281 «Public Administration» in order to balance professional competencies, their consistency with the needs of public administration, as well as review educational and professional programs and curricula containing a list of disciplines that provide different competencies.
Political institutions and public administration (General)
Many South African secondary cities depend on a single economic sector, often mining or manufacturing. This makes them vulnerable to economic change and national decision-making. We describe change in three secondary cities—Emalahleni, Matjhabeng and Newcastle—all at different phases of economic transition due to imminent mine closure. We investigate the way local governance and planning are dealing with the change. We draw on concepts from institutional economics and evolutionary governance theory, material from strategic planning documents, and approximately 50 key informant interviews. We show how difficult it is to steer economic planning during economic transitions, and we demonstrate how both economic change and governance are path-dependent. Path dependency in South Africa’s mining towns has several causes: the colonial influence, which emphasised extraction and neglected beneficiation; the dominance of a single sector; the long-term problems created by mining; and the lack of the skills needed to bring about economic change. The local governments’ continuing reliance on the New Public Management paradigm, which focuses on steering as opposed to building networks, compounds the problem, along with poor governance, inadequate local capacity and inappropriate intergovernmental relations. Of the three towns, only Newcastle has shown signs of taking a new path.
Itamar Rickover, Ofra Ben Ishai, Ayala Keissar-Sugarman
In recent years, Israel has witnessed two significant processes that challenge the dominant republican discourse that prioritizes military over national-civic service (known as The Israeli national-civilian service—NCS)in terms of contributing the constitution of citizenship and of the material and symbolic convertibility offered to service candidates. The first is related to the expanding range of roles offered in the NCS. The second, related process, which is our current focus, occurs among young religious women from the urban upper-middle class who respond to this expansion by seeking to serve in technological roles, given their high qualifications. Combined, these processes transform the status of the NCS and accelerate the de-monopolization of military service. To examine the contribution of religious young women to the change in the status of service in Israel, we conducted a narrative analysis of interviews with service candidates. Our analysis revealed their strategic use of four different discourses: the neo-liberal economic discourse, the liberal rights and self-realization discourse, the ethnonational discourse, and the religious gender discourse. The way the participants negotiated the four discourses to justify their selection of either military or national-civic service structured their agency as actors transforming the power equation between the two types of service.