Hasil untuk "Political Science"

W. Jennings, T. Shibamoto

R. Jakobson

M. Fiorina

Andrew Collier

J. Klein

Rogers M. Smith

Boehm, M. Fisher

Are humans by nature hierarchical or egalitarian? "Hierarchy in the Forest" addresses this question by examining the evolutionary origins of social and political behavior. Christopher Boehm, an anthropologist whose fieldwork has focused on the political arrangements of human and nonhuman primate groups, postulates that egalitarianism is in effect a hierarchy in which the weak combine forces to dominate the strong.The political flexibility of our species is formidable: we can be quite egalitarian, we can be quite despotic. "Hierarchy in the Forest" traces the roots of these contradictory traits in chimpanzee, bonobo, gorilla, and early human societies. Boehm looks at the loose group structures of hunter-gatherers, then at tribal segmentation, and finally at present-day governments to see how these conflicting tendencies are reflected."Hierarchy in the Forest" claims new territory for biological anthropology and evolutionary biology by extending the domain of these sciences into a crucial aspect of human political and social behavior. This book will be a key document in the study of the evolutionary basis of genuine altruism.

مرتضی سیاره, زهرا دهقان شبانی, کریم اسلاملوئیان et al.

در دهه‌های اخیر، ساختار اقتصادی و ژئوپلیتیکی جهان دستخوش تحولاتی عمیق شده است، ازجمله ظهور قدرت‌های نوظهور و حرکت به‌سوی نظمی چندقطبی. در این چارچوب، پیوستن جمهوری اسلامی ایران به گروه بریکس در ژانویة ۲۰۲۴م، نشانه‌ای از تعمیق همکاری میان اقتصادهای نوظهور و فرصتی راهبردی برای تقویت همکاری‌های اقتصادی، دسترسی به بازارهای بزرگ‌تر، تسهیل تجارت با ارزهای ملی و کاهش اثر تحریم‌ها فراهم می‌آورد. درحالی‌که بیشتر مطالعات پیشین به تحلیل‌های کلی محدود است، این پژوهش با هدف پرکردن خلأ موجود، آثار رفاهی و تجاری این عضویت را در سطح کالایی بررسی می‌کند. در پاسخ به خلأ مطالعات دقیق در زمینة آثار تجاری عضویت ایران در بریکس، در این پژوهش با بهره‌گیری از مدل تعادل جزئی جهانی (GSIM) و داده‌های ۸۶۰ کد کالای HS4 دارای مزیت نسبی، آثار چهار سناریوی کاهش تعرفه تحلیل شده است. داده‌ها از پایگاه‌های UN Comtrade و MacMap استخراج و با پایتون پردازش شده‌ است. نتایج نشان می‌دهد آزادسازی کامل تجاری برای ایران 13/1 میلیارد دلار رفاه خالص به‌همراه دارد و نسبت ایجاد تجارت به انحراف تجارت (71/2) گویای منافع در خور توجه است. بخش‌هایی مانند معدن، پلاستیک و ماشین‌آلات بیشترین سود را می‌برند، درحالی‌که محصولات نباتی (مانند ذرت) با ضرر مواجه می‌شوند. تدوین راهبرد تجاری هدف‌مند با حذف کالاهای زیان‌ده و تعیین سطوح بهینة تعرفة خالص، رفاه ایران را تا 2/27 درصد افزایش خواهد داد و زمینه‌ساز بهره‌گیری حداکثری از فرصت‌های عضویت در بریکس خواهد بود. مشارکت فعال در بریکس با تغییر مسیر تجارت از شرکای محدود به بازارهای متنوع‌تر درون‌گروهی، تاب‌آوری اقتصادی ایران را افزایش می‌دهد.

Venkat Ram Reddy Ganuthula, Krishna Kumar Balaraman

This article examines the complex relationship between money and political legitimacy in democracies (United States, Germany, India) and nondemocracies (China, Russia), using published empirical evidence to explore how financial resources influence governance. In democracies, US campaign finance, German party funding, and Indias electoral bonds amplify elite influence, openly eroding public trust by skewing policy toward wealthy interests. In nondemocracies, Chinas state enterprise patronage and Russias oligarch suppression strengthen legitimacy, yet hide vulnerabilities revealed by anticorruption campaigns and power struggles. The analysis argues that moneys corrosive impact is widespread but varies: democracies face evident legitimacy crises, while nondemocracies conceal underlying fragility. These findings highlight the need for reforms: increased transparency in democracies and wider power bases in nondemocracies, to mitigate moneys distorting effect on political authority.

Gennadi Malaschonok, Roman Sakh

The article provides a brief description of the MathPartner service. This freely available cloud-based Mathematics is a universal system for symbolic-numeric calculations. Its Mathpar language is a subset of the LaTeX language, but allows you to create mathematical texts that contain "computable" mathematical operators. This opens up completely new opportunities for improving the educational process for all natural science disciplines, for the use of mathematics in scientific and engineering calculations. To save and freely exchange educational and other texts in the Mathpar language, a GitHub repository has been created. It is concluded that cloud mathematics MathPartner is a new breakthrough technology for school and university natural science education, for scientific and engineering applications.

Peyman Rostami, Vahid Rahimzadeh, Ali Adibi et al.

Stance detection identifies the viewpoint expressed in text toward a specific target, such as a political figure. While previous datasets have focused primarily on tweet-level stances from established platforms, user-level stance resources, especially on emerging platforms like Bluesky remain scarce. User-level stance detection provides a more holistic view by considering a user's complete posting history rather than isolated posts. We present the first stance detection dataset for the 2024 U.S. presidential election, collected from Bluesky and centered on Kamala Harris and Donald Trump. The dataset comprises 16,044 user-target stance pairs enriched with engagement metadata, interaction graphs, and user posting histories. PolitiSky24 was created using a carefully evaluated pipeline combining advanced information retrieval and large language models, which generates stance labels with supporting rationales and text spans for transparency. The labeling approach achieves 81\% accuracy with scalable LLMs. This resource addresses gaps in political stance analysis through its timeliness, open-data nature, and user-level perspective. The dataset is available at https://doi.org/10.5281/zenodo.15616911

علی اشرف نظری, رضا نظرپور

پس از پیروزی انقلاب اسلامی، ائتلاف میان گفتمان‌های انقلابی به افتراق مبدل گردید. در این دوران حاملان گفتمان اسلام سیاسی- فقاهتی درون حاکمیت و اسلام انقلابی مجاهدین خلق، هر یک، مبنا و غایت انقلاب را متفاوت با دیگری تعریف نموده و خود را عامل و رهبر انقلاب و دیگری را مانعی در جهت نیل به اهداف انقلاب می‌­دانست. این امر به ناگزیر آنان را در تقابل با یکدیگر قرار داد. هر چه شدت و جدیت این تقابل افزایش یافت، خشونت نیز تشدید گردید. تقابل آنتاگونیستی میان نیروهای سیاسی و حاکمیت، خسارات فراوان سیاسی و انسانی به انقلاب نوپای ایران وارد آورد. پرسش پژوهش حاضر این است که دلایل تقابل آنتاگونیستی میان حاکمیت و مجاهدین خلق چه بوده است؟ این پژوهش در راستای پاسخ به این سؤال از نظریه و روش تحلیل گفتمان لاکلائو و موف استفاده کرده است. در این پژوهش نشان داده شده است هر یک از گفتمان‌های اسلام سیاسی- فقاهتی و اسلام انقلابی مجاهدین، «دیگری» را به‌عنوان «بیگانه» و «دشمن» انقلاب تلقی نموده و درصدد طرد و حذف آن برآمد؛ بدین ترتیب میان دو دشمن نبرد مسلحانه به وقوع پیوست.

Jinyi Ye, Luca Luceri, Emilio Ferrara

Approximately 50% of tweets in X's user timelines are personalized recommendations from accounts they do not follow. This raises a critical question: What political content are users exposed to beyond their established networks, and what implications does this have for democratic discourse online? In this paper, we present a six-week audit of X's algorithmic content recommendations during the 2024 U.S. Presidential Election by deploying 120 sock-puppet monitoring accounts to capture tweets from their personalized "For You" timelines. Our objective is to quantify out-of-network content exposure for right- and left-leaning user profiles and assess any potential inequalities and biases in political exposure. Our findings indicate that X's algorithm skews exposure toward a few high-popularity accounts across all users, with right-leaning users experiencing the highest level of exposure inequality. Both left- and right-leaning users encounter amplified exposure to accounts aligned with their own political views and reduced exposure to opposing viewpoints. Additionally, we observe that new accounts experience a right-leaning bias in exposure within their default timelines. Our work contributes to understanding how content recommendation systems may induce and reinforce biases while exacerbating vulnerabilities among politically polarized user groups. We underscore the importance of transparency-aware algorithms in addressing critical issues such as safeguarding election integrity and fostering a more informed digital public sphere.

Dairazalia Sánchez-Cortés, Sergio Burdisso, Esaú Villatoro-Tello et al.

Bias assessment of news sources is paramount for professionals, organizations, and researchers who rely on truthful evidence for information gathering and reporting. While certain bias indicators are discernible from content analysis, descriptors like political bias and fake news pose greater challenges. In this paper, we propose an extension to a recently presented news media reliability estimation method that focuses on modeling outlets and their longitudinal web interactions. Concretely, we assess the classification performance of four reinforcement learning strategies on a large news media hyperlink graph. Our experiments, targeting two challenging bias descriptors, factual reporting and political bias, showed a significant performance improvement at the source media level. Additionally, we validate our methods on the CLEF 2023 CheckThat! Lab challenge, outperforming the reported results in both, F1-score and the official MAE metric. Furthermore, we contribute by releasing the largest annotated dataset of news source media, categorized with factual reporting and political bias labels. Our findings suggest that profiling news media sources based on their hyperlink interactions over time is feasible, offering a bird's-eye view of evolving media landscapes.

Simon Münker

Contemporary research in social sciences increasingly utilizes state-of-the-art generative language models to annotate or generate content. While these models achieve benchmark-leading performance on common language tasks, their application to novel out-of-domain tasks remains insufficiently explored. To address this gap, we investigate how personalized language models align with human responses on the Moral Foundation Theory Questionnaire. We adapt open-source generative language models to different political personas and repeatedly survey these models to generate synthetic data sets where model-persona combinations define our sub-populations. Our analysis reveals that models produce inconsistent results across multiple repetitions, yielding high response variance. Furthermore, the alignment between synthetic data and corresponding human data from psychological studies shows a weak correlation, with conservative persona-prompted models particularly failing to align with actual conservative populations. These results suggest that language models struggle to coherently represent ideologies through in-context prompting due to their alignment process. Thus, using language models to simulate social interactions requires measurable improvements in in-context optimization or parameter manipulation to align with psychological and sociological stereotypes properly.

Tamar Zeilberger

Existing research has established that autocrats offer concessions to prevent ouster by their inner circle. This paper examines how those concessions are influenced by the relative uncertainty of an autocrat's inner circle about remaining in that favored body. I take as my starting point the formal model of political survival presented in Bueno de Mesquita et al.'s The Logic of Political Survival. I extend the model to account for variation in the relative uncertainty of an autocrat's inner circle. To make the math tractable, I dispense with convention and introduce comparative statics across two models with different formulations of uncertainty. This exercise reveals a set of conditions under which to expect an increase in the concessions offered by an autocrat, with implications for development and democracy. Those findings yield a corresponding set of logical corollaries with potential to further our understanding of authoritarian politics, including an unexamined facet of the "dictator's dilemma" (Wintrobe 1990, 1998) and related incentives for members of an inner circle to permit purges or act to destabilize their ranks. The models also identify a source of policy volatility not found outside of autocracies. Taken together, the findings suggest a need for more research on elite uncertainty in autocracies.

Felipe Franz Wienke, Rafaella Fernandes de Mattos

O presente artigo tem como objetivo a análise do reconhecimento da autonomia universitária e da liberdade de cátedra como instrumentos de realização do Estado Democrático de Direito instituído pela Constituição Federal de 1988. Em um primeiro momento, contextualiza os desdobramentos do direito social fundamental à educação para a conceituação e identificação das principais características da liberdade de ensinar sob os aspectos institucional e docente. Posteriormente, propõe o estudo da autonomia universitária e da liberdade de cátedra sob a perspectiva do Estado Democrático de Direito à luz do julgamento da Ação de Descumprimento de Preceito Fundamental nº 548 pelo Supremo Tribunal Federal e da Declaração do Parlamento do MERCOSUL. Conclui-se que a garantia da liberdade de ensinar é pressuposto essencial em uma democracia. Utiliza-se pesquisa qualitativa e as técnicas bibliográfica e documental.

Shangbin Feng, Chan Young Park, Yuhan Liu et al.

Language models (LMs) are pretrained on diverse data sources, including news, discussion forums, books, and online encyclopedias. A significant portion of this data includes opinions and perspectives which, on one hand, celebrate democracy and diversity of ideas, and on the other hand are inherently socially biased. Our work develops new methods to (1) measure political biases in LMs trained on such corpora, along social and economic axes, and (2) measure the fairness of downstream NLP models trained on top of politically biased LMs. We focus on hate speech and misinformation detection, aiming to empirically quantify the effects of political (social, economic) biases in pretraining data on the fairness of high-stakes social-oriented tasks. Our findings reveal that pretrained LMs do have political leanings that reinforce the polarization present in pretraining corpora, propagating social biases into hate speech predictions and misinformation detectors. We discuss the implications of our findings for NLP research and propose future directions to mitigate unfairness.

K. I. Kellermann

I review the scientific and technical history of the Search for Extraterrestrial Intelligence (SETI), discuss the impact of the political involvement, and speculate on the nature of a successful detection and its potential social and cultural impact. Emphasis is on the development of SETI in the United States and the complementary progress in the Former Soviet Union.

Rajkumar Pujari, Chengfei Wu, Dan Goldwasser

Social media discourse frequently consists of 'seemingly similar language used by opposing sides of the political spectrum', often translating to starkly contrasting perspectives. E.g., 'thoughts and prayers', could express sympathy for mass-shooting victims, or criticize the lack of legislative action on the issue. This paper defines the context required to fully understand such ambiguous statements in a computational setting and ground them in real-world entities, actions, and attitudes. We propose two challenging datasets that require an understanding of the real-world context of the text. We benchmark these datasets against models built upon large pre-trained models, such as RoBERTa and GPT-3. Additionally, we develop and benchmark more structured models building upon existing Discourse Contextualization Framework and Political Actor Representation models. We analyze the datasets and the predictions to obtain further insights into the pragmatic language understanding challenges posed by the proposed social grounding tasks.