Hasil untuk "Political Science"

C. Hermann

Krishna Sharma, Khemraj Bhatt

Text-based measurement in political research often treats classi6ication disagreement as random noise. We examine this assumption using con6idence-weighted human annotations of 5,000 social media messages by U.S. politicians. We 6ind that political communication is generally highly legible, with mean con6idence exceeding 0.99 across message type, partisan bias, and audience classi6ications. However, systematic variation concentrates in the constituency category, which exhibits a 1.79 percentage point penalty in audience classi6ication con6idence. Given the high baseline of agreement, this penalty represents a sharp relative increase in interpretive uncertainty. Within messages, intent remains clear while audience targeting becomes ambiguous. These patterns persist with politician 6ixed effects, suggesting that measurement error in political text is structured by strategic incentives rather than idiosyncratic coder error.

Gabriela Juncosa, Saeedeh Mohammadi, Margaret Samahita et al.

Social media platforms face increasing scrutiny over the rapid spread of misinformation. In response, many have adopted community-based content moderation systems, including Community Notes (formerly Birdwatch) on X (formerly Twitter), Footnotes on TikTok, and Facebook's Community Notes initiative. However, research shows that the current design of these systems can allow political biases to influence both the development of notes and the rating processes, reducing their overall effectiveness. We hypothesize that enabling users to collaborate on writing notes, rather than relying solely on individually authored notes, can enhance their overall quality. To test this idea, we conducted an online experiment in which participants jointly authored notes on political posts. Our results show that teams produce notes that are rated as more helpful than individually written notes. We also find that politically diverse teams perform better when evaluating Republican posts, while group composition does not affect perceived note quality for Democrat posts. However, the advantage of collaboration diminishes when team members are aware of one another's political affiliations. Taken together, these findings underscore the complexity of community-based content moderation and highlight the importance of understanding group dynamics and political diversity when designing more effective moderation systems.

Olexandr Bieliaiev

The article shows the history of the life and activities of the organisers and members of the literary studio «Brama», which was founded in Kyiv in 1963 and was created to unite creative youth who did not accept Soviet rule and ideology, around the problems of creative development, preservation of national culture and opposition to the ruling regime. The biographies of the poets of the sixties such as Viktor Mohylnyi, Hryhorii Tymenko, Vasyl Solovia, artist and fashion designer Liubov Panchenko, public and political figure Oles Shevchenko, dissident and teacher Yurii Murashov and others are considered. The study should fill the gap in studies of the history of the Ukrainian dissident movement and cultural organizations, which exists due to the absence in modern Ukrainian historical science of separate works devoted to the history of the studio “Brama” and biographies of personalities who were its members. The life story of the members of the literary studio “Brama” is a direct reflection of the entire spectrum of problems and life circumstances that the Ukrainian creative intelligentsia and opposition to the Soviet totalitarian regime faced. The biographies of the individuals who made up the personal group of the studio “Brama” indicate a high level of their involvement in the socio-political and national-cultural life of Ukraine of their time, their significant contribution to the development of culture, the preservation of national identity and the fight against communist-Russian colonial rule. Thus, the biographies of the members of the studio “Brama” can be exemplary in considering the history of the dissident movement in Ukraine and the development of cultural and national life during the Soviet occupation in the second half of the twentieth century.

Panagiota Xanthopoulou, Alexandros Sahinidis, Evangelos E. Vassiliou et al.

The purpose of the study is to investigate the gap between entrepreneurial intention and entrepreneurial action among business administration students with the aim of understanding why many who develop entrepreneurial intentions do not ultimately take action. A quantitative methodology was adopted from a sample of students who took entrepreneurship courses at different stages of their studies, allowing for the mapping of changes in entrepreneurial intention over time. Findings show that although entrepreneurship education initially strengthens intention, it declines after course completion, mainly due to external constraints, perceived risk, lack of support, and differences in students’ personal backgrounds. This research confirms the existence of a significant “intention-action gap” and highlights determining factors such as self-confidence, family support, and entrepreneurial culture. The value of this study lies in its combined and quasi-longitudinal approach, which offers new insights into the conversion of intention into action and contributes to the development of educational and policy strategies to enhance student entrepreneurship.

Shiruo Hu, Wenbo Shan, Yingjia Li et al.

Hydro-Science and Engineering (Hydro-SE) is a critical and irreplaceable domain that secures human water supply, generates clean hydropower energy, and mitigates flood and drought disasters. Featuring multiple engineering objectives, Hydro-SE is an inherently interdisciplinary domain that integrates scientific knowledge with engineering expertise. This integration necessitates extensive expert collaboration in decision-making, which poses difficulties for intelligence. With the rapid advancement of large language models (LLMs), their potential application in the Hydro-SE domain is being increasingly explored. However, the knowledge and application abilities of LLMs in Hydro-SE have not been sufficiently evaluated. To address this issue, we propose the Hydro-SE LLM evaluation benchmark (Hydro-SE Bench), which contains 4,000 multiple-choice questions. Hydro-SE Bench covers nine subfields and enables evaluation of LLMs in aspects of basic conceptual knowledge, engineering application ability, and reasoning and calculation ability. The evaluation results on Hydro-SE Bench show that the accuracy values vary among 0.74 to 0.80 for commercial LLMs, and among 0.41 to 0.68 for small-parameter LLMs. While LLMs perform well in subfields closely related to natural and physical sciences, they struggle with domain-specific knowledge such as industry standards and hydraulic structures. Model scaling mainly improves reasoning and calculation abilities, but there is still great potential for LLMs to better handle problems in practical engineering application. This study highlights the strengths and weaknesses of LLMs for Hydro-SE tasks, providing model developers with clear training targets and Hydro-SE researchers with practical guidance for applying LLMs.

Ricardo Alonzo Fernández Salguero

Why have left-wing movements historically integrated participatory art forms (such as murals and protest songs) into their praxis, while right-wing movements have prioritized strategic communication and, more recently, the digital culture of memes? This article introduces the concept of aesthetic asymmetry to explain this divergence in political action. We argue that the asymmetry is not coincidental but the result of four interconnected structural factors: the organizational ecosystem, the moral and emotional framework, the material supports, and the historical tradition of each political spectrum. While the left tends to use art in a constitutive manner to forge community, solidarity, and hope, the contemporary right tends to use it instrumentally to mobilize polarizing affects such as humor and resentment. Drawing on comparative literature from the Theatre of the Oppressed to analyses of alt-right meme wars, we nuance this distinction and show how the aesthetic logic of each pole aligns with its strategic objectives. The article culminates in a prescriptive model for artistic action, synthesizing keys to effective mobilization into emotional, narrative, and formatting strategies. Understanding this asymmetry is crucial for analyzing political communication and for designing cultural interventions capable of generating profound social change.

Shreyansh Padarha

Post-World War II armed conflicts have often been viewed with higher scrutiny in order to avoid a full-scale global war. This scrutiny has led to the establishment of determinants of war such as poverty, inequalities, literacy, and many more. There is a gap that exists in probing countries in the Global South for political party fragmentation and examining ideology-driven polarization's effect on armed conflicts. This paper fills this gap by asking the question: How does political identity-induced polarization affect conflicts in the Global South region? Polarization indices are created based on socially relevant issues and party stances from the V-Party Dataset. Along with control variables, they are tested against the response variables conflict frequency and conflict severity created from the UCDP (Uppsala Conflict Data Program). Through Chow's test, Regional Structural Breaks are found between regions when accounting for polarization-conflict dynamics. A multilevel mixed effects modelling approach is used to create region-specific models to find what types of polarization affect conflict in different geographies and their adherence to normative current developments. The paper highlights that vulnerable regions of the world are prone to higher polarization-induced violence. Modelling estimates indicate polarization of party credo on Minority Rights, Rejection of Political Violence, Religious Principles, and Political Pluralism are strong proponents of cultivated violence. The Global South's inhibitions and slow progress towards development are caused by hindrances from armed conflicts; this paper's results show self-inflicted political instability and fragmentation's influence on these events, making the case for urgency in addressing and building inter-group homogeneity and tolerance.

Pietro Bernardelle, Stefano Civelli, Leon Fröhling et al.

Large language models (LLMs) are increasingly deployed in politically sensitive settings, raising concerns about their potential to encode, amplify, or be steered toward specific ideologies. We investigate how adopting synthetic personas influences ideological expression in LLMs across seven models (7B-70B+ parameters) from multiple families, using the Political Compass Test as a standardized probe. Our analysis reveals four consistent patterns: (i) larger models display broader and more polarized implicit ideological coverage; (ii) susceptibility to explicit ideological cues grows with scale; (iii) models respond more strongly to right-authoritarian than to left-libertarian priming; and (iv) thematic content in persona descriptions induces systematic and predictable ideological shifts, which amplify with size. These findings indicate that both scale and persona content shape LLM political behavior. As such systems enter decision-making, educational, and policy contexts, their latent ideological malleability demands attention to safeguard fairness, transparency, and safety.

Merve Ipek Bal, Diogo Pacheco

In an era where social media platforms are central to political communication, the activity of bots raises pressing concerns about amplification, manipulation, and misinformation. Drawing on more than 315 million tweets posted from August 2018 to June 2022, we examine behavioural patterns, sentiment dynamics, and the thematic focus of bot- versus human-generated content spanning the 2018 Brazilian presidential election and the lead-up to the 2022 contest. Our analysis shows that bots relied disproportionately on retweets and replies, with reply activity spiking after the 2018 election, suggesting tactics of conversational infiltration and amplification. Sentiment analysis indicates that bots maintained a narrower emotional tone, in contrast to humans, whose sentiment fluctuated more strongly with political events. Topic modelling further reveals bots' repetitive, Bolsonaro-centric messaging, while human users engaged with a broader range of candidates, civic concerns, and personal reflections. These findings underscore bots' role as amplifiers of narrow agendas and their potential to distort online political discourse.

W. Russell Neuman, Chad Coleman, Ali Dasdan et al.

Recent studies have revealed a consistent liberal orientation in the ethical and political responses generated by most commercial large language models (LLMs), yet the underlying causes and resulting implications remain unclear. This paper systematically investigates the political temperament of seven prominent LLMs - OpenAI's GPT-4o, Anthropic's Claude Sonnet 4, Perplexity (Sonar Large), Google's Gemini 2.5 Flash, Meta AI's Llama 4, Mistral 7b Le Chat and High-Flyer's DeepSeek R1 -- using a multi-pronged approach that includes Moral Foundations Theory, a dozen established political ideology scales and a new index of current political controversies. We find strong and consistent prioritization of liberal-leaning values, particularly care and fairness, across most models. Further analysis attributes this trend to four overlapping factors: Liberal-leaning training corpora, reinforcement learning from human feedback (RLHF), the dominance of liberal frameworks in academic ethical discourse and safety-driven fine-tuning practices. We also distinguish between political "bias" and legitimate epistemic differences, cautioning against conflating the two. A comparison of base and fine-tuned model pairs reveals that fine-tuning generally increases liberal lean, an effect confirmed through both self-report and empirical testing. We argue that this "liberal tilt" is not a programming error or the personal preference of programmers but an emergent property of training on democratic rights-focused discourse. Finally, we propose that LLMs may indirectly echo John Rawls' famous veil-of ignorance philosophical aspiration, reflecting a moral stance unanchored to personal identity or interest. Rather than undermining democratic discourse, this pattern may offer a new lens through which to examine collective reasoning.

Bogumiła Mucha-Leszko, Aleksandra Gawlikowska-Fyk

The article consists of two main parts: theoretical and empirical. The first presents the opinions of authors representing International Political Economy who question the assumption that the transformation of the energy sector can be based only on the market mechanism. They justify that the most favourable conditions for carrying out structural changes going in the direction of gradual reduction of fossil fuels and increasing the share of alternative sources in energy production exist in democratic countries where the state performs social-economic functions and engages in the promotion of clean technologies in the energy sector. The empirical analysis assesses the advancement of structural changes in energy production globally and in the group of the 15 largest producers by the share of raw materials used in 2000-2019. The study also includes changes in the subject and object structure of exports and imports of energy raw materials. The analysis shows that diversification of energy sources was progressing, but mainly concerned the share of coal, oil and gas in its production. A negative phenomenon was the increase in the share of coal in energy production in the global economy from 22.8 to 27.1%. Contributing to this were: China, Australia, Indonesia, India and Russia. In the EU, the results of the transformation of the energy sector, compared to other countries, were exceptionally good. The share of renewable raw materials in energy production increased from 11.0 to 33.4%.             Referring to the impact of the energy sector transformation and changes in energy markets on the global balance of economic power, the authors concluded that fossil raw material resources, which are concentrated in a small group of countries, give them great opportunities to directly influence the formation of their world prices, the situation in other markets and the overall situation in the global economy through the transmission of inflation, exchange rate fluctuations and imbalances in the balance of payments. In 2000-2019, Russia strengthened its dominance in global energy commodity markets by increasing its share in world exports from 10 to 12.7%, against 9.1% of the USA, 8.8% of the EU-28, 7.5% of Saudi Arabia and 6.05% of Australia. Russia's important assets as an energy power are its well-developed oil and gas transport infrastructure covering Europe and Asia, the strong monopolistic position of Gazprom and other companies on the fossil fuel markets and the EU's high dependence on imports of energy resources from Russia. The new geopolitical situation has a major impact on changing the priorities of the energy policy of the EU and the US, as well as of the countries dependent on raw material imports from Russia. The process of transformation of the energy sector may be dynamised, which will weaken Russia's position.

Peipei Chen, Jianguo Dai, Guoshun Zhang et al.

Nitrogen plays a crucial role in cotton growth, making the precise diagnosis of its nutrition levels vital for the scientific and rational application of fertilizers. Addressing this need, our study introduced an EMRDFC-based diagnosis model specifically for cotton nitrogen nutrition levels. In our field experiments, cotton was subjected to five different nitrogen application rates. To enhance the diagnostic capabilities of our model, we employed ResNet101, MobileNetV2, and DenseNet121 as base models and integrated the CBAM (Convolutional Block Attention Module) into each to improve their ability to differentiate among various nitrogen levels. Additionally, the Focal loss function was introduced to address issues of data imbalance. The model’s effectiveness was further augmented by employing integration strategies such as relative majority voting, simple averaging, and weighted averaging. Our experimental results indicated significant accuracy improvements in the enhanced ResNet101, MobileNetV2, and DenseNet121 models by 2.3%, 2.91%, and 2.93%, respectively. Notably, the integration of these models consistently improved accuracy, with gains of 0.87% and 1.73% compared to the highest-performing single model, DenseNet121FC. The optimal ensemble model, which utilized the weighted average method, demonstrated superior learning and generalization capabilities. The proposed EMRDFC model shows great promise in precisely identifying cotton nitrogen status, offering critical insights into the diagnosis of crop nutrient status. This research contributes significantly to the field of agricultural technology by providing a reliable tool for nitrogen-level assessment in cotton cultivation.

Thanasis Troboukis, Kelly Kiki, Antonis Galanopoulos et al.

This chapter introduces a research project titled "Analyzing the Political Discourse: A Collaboration Between Humans and Artificial Intelligence", which was initiated in preparation for Greece's 2023 general elections. The project focused on the analysis of political leaders' campaign speeches, employing Artificial Intelligence (AI), in conjunction with an interdisciplinary team comprising journalists, a political scientist, and data scientists. The chapter delves into various aspects of political discourse analysis, including sentiment analysis, polarization, populism, topic detection, and Named Entities Recognition (NER). This experimental study investigates the capabilities of large language model (LLMs), and in particular OpenAI's ChatGPT, for analyzing political speech, evaluates its strengths and weaknesses, and highlights the essential role of human oversight in using AI in journalism projects and potentially other societal sectors. The project stands as an innovative example of human-AI collaboration (known also as "hybrid intelligence") within the realm of digital humanities, offering valuable insights for future initiatives.

Michael Haman, Milan Školník

This study examines the political bias of chatbots powered by large language models, namely ChatGPT and Gemini, in the context of the 2024 European Parliament elections. The research focused on the evaluation of political parties represented in the European Parliament across 27 EU Member States by these generative artificial intelligence (AI) systems. The methodology involved daily data collection through standardized prompts on both platforms. The results revealed a stark contrast: while Gemini mostly refused to answer political questions, ChatGPT provided consistent ratings. The analysis showed a significant bias in ChatGPT in favor of left-wing and centrist parties, with the highest ratings for the Greens/European Free Alliance. In contrast, right-wing parties, particularly the Identity and Democracy group, received the lowest ratings. The study identified key factors influencing the ratings, including attitudes toward European integration and perceptions of democratic values. The findings highlight the need for a critical approach to information provided by generative AI systems in a political context and call for more transparency and regulation in this area.

Nouar AlDahoul, Talal Rahwan, Yasir Zaki

A quarter of US adults regularly get their news from YouTube. Yet, despite the massive political content available on the platform, to date no classifier has been proposed to identify the political leaning of YouTube videos. To fill this gap, we propose a novel classifier based on Bert -- a language model from Google -- to classify YouTube videos merely based on their titles into six categories, namely: Far Left, Left, Center, Anti-Woke, Right, and Far Right. We used a public dataset of 10 million YouTube video titles (under various categories) to train and validate the proposed classifier. We compare the classifier against several alternatives that we trained on the same dataset, revealing that our classifier achieves the highest accuracy (75%) and the highest F1 score (77%). To further validate the classification performance, we collect videos from YouTube channels of numerous prominent news agencies, such as Fox News and New York Times, which have widely known political leanings, and apply our classifier to their video titles. For the vast majority of cases, the predicted political leaning matches that of the news agency.

Jillian Fisher, Shangbin Feng, Robert Aron et al.

As modern large language models (LLMs) become integral to everyday tasks, concerns about their inherent biases and their potential impact on human decision-making have emerged. While bias in models are well-documented, less is known about how these biases influence human decisions. This paper presents two interactive experiments investigating the effects of partisan bias in LLMs on political opinions and decision-making. Participants interacted freely with either a biased liberal, biased conservative, or unbiased control model while completing these tasks. We found that participants exposed to partisan biased models were significantly more likely to adopt opinions and make decisions which matched the LLM's bias. Even more surprising, this influence was seen when the model bias and personal political partisanship of the participant were opposite. However, we also discovered that prior knowledge of AI was weakly correlated with a reduction of the impact of the bias, highlighting the possible importance of AI education for robust mitigation of bias effects. Our findings not only highlight the critical effects of interacting with biased LLMs and its ability to impact public discourse and political conduct, but also highlights potential techniques for mitigating these risks in the future.

Peter Wilkin

The article examines the concept of geoculture understood as a form of dominant ideology in the twenty-first century. It situates this in the context of the attempt by conservative and liberal elites in the core states to frame a coherent understanding of the post-Cold War world with which to guide, justify, and legitimize policies and actions. The dominant geoculture has come to be framed by two contrasting grand narratives which establish a framework for legitimate intra-elite debate and understanding of the post-Cold War era: Neoliberalism and the Clash of Civilizations. The significance of these two intra-elite grand narratives is that they represent a break with what Wallerstein has called “centrist liberalism,” which has tended to dominate the geoculture of the modern world-system.

Grigoreva Julia G.

Introduction. The relevance of the study is determined by the increasing role of North-East Asia as one of the world and political centers, as well as the growth of various challenges and threats in the region, affecting safe and stable development of the world community as a whole. The study of the problem of regional security in Northeast Asia and the participation of Mongolia in its ensuring is important for the formation of theoretical and practical conclusions and assessments regarding its international status. Since the 1980s Mongolia has been consistently pursuing the policy of creating a mechanism for dialogue in Northeastern Asia. The result of these efforts was the Ulaanbaatar Northeast Asia Security Dialogue initiative. Mongolia's active foreign policy and the will to fully participate in regional cooperation in Northeastern Asia and in as many international and multilateral organizations as possible is one of the hallmarks of the phenomenon of modern Mongolia. The purpose of the study is to review the “Ulaanbaatar Dialogue on Security in Northeast Asia” and analyze its role in creating conditions for the interaction of all stakeholders in the interests of maintaining peace in Northeastern Asia. Results. This study presents a brief history of the formation and development of the Ulaanbaatar Dialogue, identifies advantages over similar discussion platforms in the region, and shows the importance of this event in increasing the international status of Mongolia. It is concluded that in the nearest future Mongolia may become an analogue of Asian Switzerland, the main platform for negotiations between countries in Northeast Asia due to the fact that Ulaanbaatar pursues an open, multifaceted foreign policy, and the adherence of this country to the “third neighbor” doctrine makes Mongolia a neutral state that does not participate in military-political blocks.

Arttu Malkamäki, Ted Hsuan Yun Chen, Antti Gronow et al.

Coalitions are central to politics, including government formation, international relations, and public policy. Coalitions emerge when actors engage one another across multiple relational contexts, but existing literature often approaches coalitions in singular contexts. We introduce complex coalitions, a theoretical-methodological framework that emphasises the relevance of multiple contexts and cross-context dependencies in coalition politics. We also implement tools to statistically infer such coalition structures using multilayer networks. To demonstrate the usefulness of our approach, we compare coalitions among Finnish organisations engaging in climate politics across three con-texts: resource coordination, legacy media discourse, and social media communication. We show that considering coalitions as complex and accounting for cross-context dependencies improves the empirical validity of coalition studies. In our case study, the three contexts represent complementary, but not congruent, channels for enacting coalitions. In conclusion, we argue that the complex coalitions approach is useful for advancing understanding of coalitions in different political realms.