Hasil untuk "Political Science"

Abrar Ahmad

D. Simberloff, R. Levins, R. Lewontin

P. Rosenau

Post-modernism offers a revolutionary approach to the study of society: in questioning the validity of modern science and the notion of objective knowledge, this movement discards history, rejects humanism, and resists any truth claims. In this comprehensive assessment of post-modernism, Pauline Rosenau traces its origins in the humanities and describes how its key concepts are today being applied to, and are restructuring, the social sciences. Serving as neither an opponent nor an apologist for the movement, she cuts through post-modernism's often incomprehensible jargon in order to offer all readers a lucid exposition of its propositions. Rosenau shows how the post-modern challenge to reason and rational organization radiates across academic fields. For example, in psychology it questions the conscious, logical, coherent subject; in public administration it encourages a retreat from central planning and from reliance on specialists; in political science it calls into question the authority of hierarchical, bureaucratic decision-making structures that function in carefully defined spheres; in anthropology it inspires the protection of local, primitive cultures from First World attempts to reorganize them. In all of the social sciences, she argues, post-modernism repudiates representative democracy and plays havoc with the very meaning of "left-wing" and "right-wing." Rosenau also highlights how post-modernism has inspired a new generation of social movements, ranging from New Age sensitivities to Third World fundamentalism. In weighing its strengths and weaknesses, the author examines two major tendencies within post-modernism, the largely European, skeptical form and the predominantly Anglo-North-American form, which suggests alternative political, social, and cultural projects. She draws examples from anthropology, economics, geography, history, international relations, law, planning, political science, psychology, sociology, urban studies, and women's studies, and provides a glossary of post-modern terms to assist the uninitiated reader with special meanings not found in standard dictionaries.

R. Friedrich

B. B. D. Mesquita

R. Bhaskar

S. Harding

K. Antelman

Although many authors believe that their work has a greater research impact if it is freely available, studies to demonstrate that impact are few. This study looks at articles in four disciplines at varying stages of adoption of open access—philosophy, political science, electrical and electronic engineering and mathematics—to see whether they have a greater impact as measured by citations in the ISI Web of Science database when their authors make them freely available on the Internet. The finding is that, across all four disciplines, freely available articles do have a greater research impact. Shedding light on this category of open access reveals that scholars in diverse disciplines are adopting open-access practices and being rewarded for it.

R. Barr

Thanai Permpul, Abdeel Kadir Bello, Ahmad Abdalaziz Alnusfir et al.

The article aimed to comprehensively analyse the great powers' rivalries in the current international political and geopolitical landscape, which may be leading to a changing global order. Great Power in the Changing International Order refers to the intensifying competition and conflict among the major powers, especially the US, China, and Russia. It covers various issues such as trade, technology, security, human rights and global governance. The emergence of this rivalry has challenged the existing international order, shaped mainly by the US and its allies after the Cold War. It has created new opportunities and risks for the middle and smaller powers caught between the great-power axis. The latter half of the 20th century saw a shift toward a multipolar world due to globalisation, the collapse of the Soviet Union, and technological advances. However, the 19th and 20th centuries were marked by the dynamic nature of multipolarity, with periods of stability and instability. The receding US influence, the rise of other power centres, and the transition from geopolitics to geoeconomics are among the main factors driving the transition in the world order.   Bibliography Entry Permpul, Thanai, Abdeel Kadir Bello, Ahmad Abdalaziz Alnusfir and Meshal Abdullah Salman Almaliki. 2025. "Great Power Rivalry in a Changing International Order." Margalla Papers 29 (2): 54-67.

Jieying Chen, Karen de Jong, Andreas Poole et al.

As large language models (LLMs) become deeply embedded in digital platforms and decision-making systems, concerns about their political biases have grown. While substantial work has examined social biases such as gender and race, systematic studies of political bias remain limited, despite their direct societal impact. This paper introduces a general methodology for constructing political bias benchmarks by aligning model-generated voting predictions with verified parliamentary voting records. We instantiate this methodology in three national case studies: PoliBiasNL (2,701 Dutch parliamentary motions and votes from 15 political parties), PoliBiasNO (10,584 motions and votes from 9 Norwegian parties), and PoliBiasES (2,480 motions and votes from 10 Spanish parties). Across these benchmarks, we assess ideological tendencies and political entity bias in LLM behavior. As part of our evaluation framework, we also propose a method to visualize the ideology of LLMs and political parties in a shared two-dimensional CHES (Chapel Hill Expert Survey) space by linking their voting-based positions to the CHES dimensions, enabling direct and interpretable comparisons between models and real-world political actors. Our experiments reveal fine-grained ideological distinctions: state-of-the-art LLMs consistently display left-leaning or centrist tendencies, alongside clear negative biases toward right-conservative parties. These findings highlight the value of transparent, cross-national evaluation grounded in real parliamentary behavior for understanding and auditing political bias in modern LLMs.

J. Peck

K. Birch

Current debates in science and technology studies emphasize that the bio-economy—or, the articulation of capitalism and biotechnology—is built on notions of commodity production, commodification, and materiality, emphasizing that it is possible to derive value from body parts, molecular and cellular tissues, biological processes, and so on. What is missing from these perspectives, however, is consideration of the political-economic actors, knowledges, and practices involved in the creation and management of value. As part of a rethinking of value in the bio-economy, this article analyzes three key political-economic processes: financialization, capitalization, and assetization. In doing so, it argues that value is managed as part of a series of valuation practices, it is not inherent in biological materialities.

D. Béland

Valeria Widler, Barbara Kaminska, Andre C. R. Martins et al.

The increasing polarization in democratic societies is an emergent outcome of political opinion dynamics. Yet, the fundamental mechanisms behind the formation of political opinions, from individual beliefs to collective consensus, remain unknown. Understanding that a causal mechanism must account for both bottom-up and top-down influences, we conceptualize political opinion dynamics as hierarchical coarse-graining, where microscale opinions integrate into a macro-scale state variable. Using the CODA (Continuous Opinions Discrete Actions) model, we simulate Bayesian opinion updating, social identity-based information integration, and migration between social identity groups to represent higher-level connectivity. This results in coarse-graining across micro, meso, and macro levels. Our findings show that higher-level connectivity shapes information integration, yielding three regimes: independent (disconnected, local convergence), parallel (fast, global convergence), and iterative (slow, stepwise convergence). In the iterative regime, low connectivity fosters transient diversity, indicating an informed consensus. In all regimes, time-scale separation leads to downward causation, where agents converge on the aggregate majority choice, driving consensus. Critically, any degree of coherent higher-level information integration can overcome misalignment via global downward causation. The results highlight how emergent properties of the causal mechanism, such as downward causation, are essential for consensus and may inform more precise investigations into polarized political discourse.

Duško Jojić, Franjo Šarčević

Modeling political structures by simplicial complexes, we investigate whether introducing a mediator into a substructure increases or decreases the stability of the overall structure. We prove theorems that quantify the stability of a political structure when $n$ mediators are introduced, either one by one or simultaneously. We also examine how the stability is affected when a single agent is split into two. In addition, stability is expressed in terms of the $h$-vector, and special attention is given to a class of political structures modeled by shellable simplicial complexes. In the latter context, we analyze weighted political structures and examples of political structures modeled by independence complexes of graphs. This approach provides a rigorous, stepwise analysis of stability under different structural modifications, showing how the combinatorial and topological properties of the simplicial complex govern the structure's stability.

Hasti Narimanzadeh, Arash Badie-Modiri, Iuliia Smirnova et al.

Affective polarization and political sorting drive public antagonism around issues at the science-policy nexus. Looking at the COVID-19 period, we study cross-domain spillover of incivility and contentiousness in public engagements with climate change and public health on Twitter and Reddit. We find strong evidence of the signatures of affective polarization surrounding COVID-19 spilling into the climate change domain. Across different social media systems, COVID-19 content is associated with incivility and contentiousness in climate discussions. These patterns of increased antagonism were responsive to pandemic events that made the link between science and public policy more salient. The observed spillover activated along pre-pandemic political cleavages, specifically anti-internationalist populist beliefs, that linked climate policy opposition to vaccine hesitancy. Our findings show how affective polarization in public engagement with science becomes entrenched across science policy domains.

Jinrui Yang, Xudong Han, Timothy Baldwin

We introduce EuroParlVote, a novel benchmark for evaluating large language models (LLMs) in politically sensitive contexts. It links European Parliament debate speeches to roll-call vote outcomes and includes rich demographic metadata for each Member of the European Parliament (MEP), such as gender, age, country, and political group. Using EuroParlVote, we evaluate state-of-the-art LLMs on two tasks -- gender classification and vote prediction -- revealing consistent patterns of bias. We find that LLMs frequently misclassify female MEPs as male and demonstrate reduced accuracy when simulating votes for female speakers. Politically, LLMs tend to favor centrist groups while underperforming on both far-left and far-right ones. Proprietary models like GPT-4o outperform open-weight alternatives in terms of both robustness and fairness. We release the EuroParlVote dataset, code, and demo to support future research on fairness and accountability in NLP within political contexts.

Franziska Weeber, Tanise Ceron, Sebastian Padó

Public opinion surveys show cross-cultural differences in political opinions between socio-cultural contexts. However, there is no clear evidence whether these differences translate to cross-lingual differences in multilingual large language models (MLLMs). We analyze whether opinions transfer between languages or whether there are separate opinions for each language in MLLMs of various sizes across five Western languages. We evaluate MLLMs' opinions by prompting them to report their (dis)agreement with political statements from voting advice applications. To better understand the interaction between languages in the models, we evaluate them both before and after aligning them with more left or right views using direct preference optimization and English alignment data only. Our findings reveal that unaligned models show only very few significant cross-lingual differences in the political opinions they reflect. The political alignment shifts opinions almost uniformly across all five languages. We conclude that in Western language contexts, political opinions transfer between languages, demonstrating the challenges in achieving explicit socio-linguistic, cultural, and political alignment of MLLMs.

Patrick Parschan, Charlott Jakob

This article presents the first systematic review of unsupervised and semi-supervised computational text-based ideal point estimation (CT-IPE) algorithms, methods designed to infer latent political positions from textual data. These algorithms are widely used in political science, communication, computational social science, and computer science to estimate ideological preferences from parliamentary speeches, party manifestos, and social media. Over the past two decades, their development has closely followed broader NLP trends -- beginning with word-frequency models and most recently turning to large language models (LLMs). While this trajectory has greatly expanded the methodological toolkit, it has also produced a fragmented field that lacks systematic comparison and clear guidance for applied use. To address this gap, we identified 25 CT-IPE algorithms through a systematic literature review and conducted a manual content analysis of their modeling assumptions and development contexts. To compare them meaningfully, we introduce a conceptual framework that distinguishes how algorithms generate, capture, and aggregate textual variance. On this basis, we identify four methodological families -- word-frequency, topic modeling, word embedding, and LLM-based approaches -- and critically assess their assumptions, interpretability, scalability, and limitations. Our review offers three contributions. First, it provides a structured synthesis of two decades of algorithm development, clarifying how diverse methods relate to one another. Second, it translates these insights into practical guidance for applied researchers, highlighting trade-offs in transparency, technical requirements, and validation strategies that shape algorithm choice. Third, it emphasizes that differences in estimation outcomes across algorithms are themselves informative, underscoring the need for systematic benchmarking.