Hasil untuk "Political science"

C. Bail, Lisa P. Argyle, Taylor W. Brown et al.

Significance Social media sites are often blamed for exacerbating political polarization by creating “echo chambers” that prevent people from being exposed to information that contradicts their preexisting beliefs. We conducted a field experiment that offered a large group of Democrats and Republicans financial compensation to follow bots that retweeted messages by elected officials and opinion leaders with opposing political views. Republican participants expressed substantially more conservative views after following a liberal Twitter bot, whereas Democrats’ attitudes became slightly more liberal after following a conservative Twitter bot—although this effect was not statistically significant. Despite several limitations, this study has important implications for the emerging field of computational social science and ongoing efforts to reduce political polarization online. There is mounting concern that social media sites contribute to political polarization by creating “echo chambers” that insulate people from opposing views about current events. We surveyed a large sample of Democrats and Republicans who visit Twitter at least three times each week about a range of social policy issues. One week later, we randomly assigned respondents to a treatment condition in which they were offered financial incentives to follow a Twitter bot for 1 month that exposed them to messages from those with opposing political ideologies (e.g., elected officials, opinion leaders, media organizations, and nonprofit groups). Respondents were resurveyed at the end of the month to measure the effect of this treatment, and at regular intervals throughout the study period to monitor treatment compliance. We find that Republicans who followed a liberal Twitter bot became substantially more conservative posttreatment. Democrats exhibited slight increases in liberal attitudes after following a conservative Twitter bot, although these effects are not statistically significant. Notwithstanding important limitations of our study, these findings have significant implications for the interdisciplinary literature on political polarization and the emerging field of computational social science.

Justin Grimmer, Brandon M Stewart

H. Simon

J. Dryzek

Arend Lijphart

T. Persson, G. Tabellini

S. V. Evera

S. Levin, Helen V. Milner, Charles Perrings

A number of trends in national and international politics greatly affect our capacity to achieve the cooperation that will be necessary to address the challenges facing society over the coming decades. These involve the interplay among partisanship and party loyalties within countries, populism, and polarization within and among nations. The trends are widespread and seem to be reshaping politics across the globe. They are inherently systems-level phenomena, involving interactions among multiple component parts and the emergence of broaderscale features; yet, they have been inadequately explored from that perspective. To make progress in understanding these issues, political-science research stands to benefit from insights from other disciplines, including evolutionary biology, systems science, and the disciplines concerned with the fair and efficient provision of public goods of all kinds, but especially those affecting the shared environment and public health. These other disciplines, in turn, stand to gain equally from the perspective developed in political science. In viewing political systems as complex adaptive systems, we can gain a new understanding of the forces that shape current trends, and how that knowledge might affect governance strategies going forward. Extreme polarization is a dangerous phenomenon that requires greater scientific attention to address effectively. This Special Feature of PNAS draws on this relatively new interdisciplinary field, featuring original joint research from collaborating political scientists and complex systems theorists. Each paper is a true partnership among the different disciplines and illustrates the benefits of closer ties between complex systems and social science. The papers explore the emergence of patterns and structures in societies and the linkages among individual behaviors and societal benefits across scales of space, time, and organizational complexity. The COVID-19 pandemic provides the most recent examples of how patterns of polarization in societies interact with our abilities to solve societal challenges. The main goal of the Special Feature is to deepen our understanding of the dynamics of political polarization and related trends, and especially the interplay among these processes at multiple scales, from the local to the international. The papers cover many different aspects of this issue and do so from different systems-level perspectives, providing a broad view of the problem. The papers explore the impact of information flow networks, the diverse nature of national governance systems, the role of the media, and the dynamics of party sorting. They pose a number of key questions. Do the dynamics of such systems follow a natural progression of polarization and collapse, similar to Schumpeter’s economic theories (1)? How do migration, globalization, and new technologies, such as the internet, affect the trends? Does an extension of Duverger’s Law (2) foreshadow a natural tendency toward polarization in nations with two-party systems, like that in the United States, undercutting Madison’s dream (3)? Duverger’s Law argues that a system like that of the United States, based on a plurality rule on a single ballot, will lead to a two-party system, while Madison hoped for a system that would “break and control the violence of faction” (3). The Special Feature arose from a series of workshops in which the issues were aired, collaborations were developed, and earlier versions of the papers received constructive feedback. It became clear from those discussions that even the definition of polarization has manifold aspects, that some degree of polarization is likely healthy in sharpening issue differences in any society, and that there have been historical fluctuations in polarization at all levels, within and among nations and peoples. What is clear, though, is that it is essential to understand the causes and consequences of polarization if we are to deal with regional, national, and global problems that we will face in the coming years. The Special Feature includes 11 individual articles, incorporating both novel research and Perspectives. In addition, Jenna Bednar (4) provides a Perspective embedding the contributions within the

Lisa P. Argyle, Ethan C. Busby, Joshua R. Gubler et al.

Generative large language models (LLMs) are incredibly useful, versatile, and promising tools. However, they will be of most use to political and social science researchers when they are used in a way that advances understanding about real human behaviors and concerns. To promote the scientific use of LLMs, we suggest that researchers in the political and social sciences need to remain focused on the scientific goal of inference. To this end, we discuss the challenges and opportunities related to scientific inference with LLMs, using validation of model output as an illustrative case for discussion. We propose a set of guidelines related to establishing the failure and success of LLMs when completing particular tasks, and discuss how we can make inferences from these observations. We conclude with a discussion of how this refocus will improve the accumulation of shared scientific knowledge about these tools and their uses in the social sciences.

Irena Šutinienė

In this article, the focus is on the memory of repressions in the Kazakhstan Lithuanian diaspora, a large part of which consists of the descendants of Lithuanians who were subject to repression. Based on data from a survey of semi-structured interviews, the interpretations, evaluations, and practices for the memorialisation and commemoration of the memory of the repressions among the representatives of the diaspora are analysed. The connections of this memory with Kazakhstan’s dominant collective memory discourses and the Lithuanian narrative of the memory of repressions are discussed. The analysis reveals how discourses of the memory of the repressions in the country impact the memory of the descendant of the migrants.

Ван Хэюн, Д.В. Татаринов

The 21st century is the “era of international organizations”. the SCO is facing a realistic dilemma of “insufficient rule orientation”, “imperfect international law system” and “uneven level of rule of law among its members”. International law has its own structural dilemma of uncertainty, which lies in structure, language and doctrine, and overturns the existing international law system. Within the framework of the SCO, the traditional normal way can’t quickly and effectively establish legislation. The argumentative paradigm is rooted in the “intersubjectivity” of the international community, reshaping the effectiveness and source scope of international law, and using this paradigm can quickly and effectively build a set of international law system for SCO. This paradigm needs value guidance in line with universal rationality. The “community with a shared future for mankind” proposed by the Chairman Xi Jinping is expected to achieve the multi-dimensional goals of common prosperity, universal security, openness and win-win results, equality and inclusiveness, and joint construction, which can provide a value orientation for the development of SCO international law. This paper focuses on the SCO, tries to elaborate the problems faced by the SCO from the perspective of international law, and puts forward the research paradigm of improving the construction of SCO international law and the value orientation of “community with a shared future for mankind” on the basis of its system, in order to further clarify the direction of efforts to build the SCO legal system. Under the guidance of the theory of community with a shared future for mankind, the SCO’s practice of argumentative international law can improve the legal system construction within the organization on the basis of maintaining regional peace, and then contribute to the SCO’s participation in world governance and the promotion of the rise of Asia. Keywords: norms, indeterminacy, argumentalism, community with a shared future, SCO

Zhenyu Wang, Dequan Wang, Yi Xu et al.

The recent wave of artificial intelligence, epitomized by large language models (LLMs),has presented opportunities and challenges for methodological innovation in political science,sparking discussions on a potential paradigm shift in the social sciences. However, how can weunderstand the impact of LLMs on knowledge production and paradigm transformation in thesocial sciences from a comprehensive perspective that integrates technology and methodology? What are LLMs' specific applications and representative innovative methods in political scienceresearch? These questions, particularly from a practical methodological standpoint, remainunderexplored. This paper proposes the "Intelligent Computing Social Modeling" (ICSM) methodto address these issues by clarifying the critical mechanisms of LLMs. ICSM leverages thestrengths of LLMs in idea synthesis and action simulation, advancing intellectual exploration inpolitical science through "simulated social construction" and "simulation validation." Bysimulating the U.S. presidential election, this study empirically demonstrates the operationalpathways and methodological advantages of ICSM. By integrating traditional social scienceparadigms, ICSM not only enhances the quantitative paradigm's capability to apply big data toassess the impact of factors but also provides qualitative paradigms with evidence for socialmechanism discovery at the individual level, offering a powerful tool that balances interpretabilityand predictability in social science research. The findings suggest that LLMs will drivemethodological innovation in political science through integration and improvement rather thandirect substitution.

Leah Cathryn Windsor

Child Impact Statements (CIS) are instrumental in helping to foreground the concerns and needs of minor community members who are too young to vote and often unable to advocate for themselves politically. While many politicians and policymakers assert they make decisions in the best interests of children, they often lack the necessary information to meaningfully accomplish this. CISs are akin to Environmental Impact Statements in that both give voice to constituents who are often under-represented in policymaking. This paper highlights an interdisciplinary collaboration between Social Science and Computer Science to create a CIS tool for policymakers and community members in Shelby County, TN. Furthermore, this type of collaboration is fruitful beyond the scope of the CIS tool. Social scientists and computer scientists can leverage their complementary skill sets in data management and data interpretation for the benefit of their communities, advance scientific knowledge, and bridge disciplinary divides within the academy.

Chenxiao Yu, Jinyi Ye, Yuangang Li et al.

While LLMs have demonstrated remarkable capabilities in text generation and reasoning, their ability to simulate human decision-making -- particularly in political contexts -- remains an open question. However, modeling voter behavior presents unique challenges due to limited voter-level data, evolving political landscapes, and the complexity of human reasoning. In this study, we develop a theory-driven, multi-step reasoning framework that integrates demographic, temporal and ideological factors to simulate voter decision-making at scale. Using synthetic personas calibrated to real-world voter data, we conduct large-scale simulations of recent U.S. presidential elections. Our method significantly improves simulation accuracy while mitigating model biases. We examine its robustness by comparing performance across different LLMs. We further investigate the challenges and constraints that arise from LLM-based political simulations. Our work provides both a scalable framework for modeling political decision-making behavior and insights into the promise and limitations of using LLMs in political science research.

Sebastián C. Parnes

Karen Zhou, Chenhao Tan

Growing literature has shown that NLP systems may encode social biases; however, the political bias of summarization models remains relatively unknown. In this work, we use an entity replacement method to investigate the portrayal of politicians in automatically generated summaries of news articles. We develop an entity-based computational framework to assess the sensitivities of several extractive and abstractive summarizers to the politicians Donald Trump and Joe Biden. We find consistent differences in these summaries upon entity replacement, such as reduced emphasis of Trump's presence in the context of the same article and a more individualistic representation of Trump with respect to the collective US government (i.e., administration). These summary dissimilarities are most prominent when the entity is heavily featured in the source article. Our characterization provides a foundation for future studies of bias in summarization and for normative discussions on the ideal qualities of automatic summaries.

Tai Le Quy, Gunnar Friege, Eirini Ntoutsi

Ensuring fairness is essential for every education system. Machine learning is increasingly supporting the education system and educational data science (EDS) domain, from decision support to educational activities and learning analytics. However, the machine learning-based decisions can be biased because the algorithms may generate the results based on students' protected attributes such as race or gender. Clustering is an important machine learning technique to explore student data in order to support the decision-maker, as well as support educational activities, such as group assignments. Therefore, ensuring high-quality clustering models along with satisfying fairness constraints are important requirements. This chapter comprehensively surveys clustering models and their fairness in EDS. We especially focus on investigating the fair clustering models applied in educational activities. These models are believed to be practical tools for analyzing students' data and ensuring fairness in EDS.

Balaram Panda

Data Science is a modern Data Intelligence practice, which is the core of many businesses and helps businesses build smart strategies around to deal with businesses challenges more efficiently. Data Science practice also helps in automating business processes using the algorithm, and it has several other benefits, which also deliver in a non-profitable framework. In regards to data science, three key components primarily influence the effective outcome of a data science project. Those are 1.Availability of Data 2.Algorithm 3.Processing power or infrastructure

Paul R. Abramson, John H. Aldrich

Róbert Király

Book review