Hasil untuk "Political science"

L. Strauss

Charles Taylor

A. Escobar

D. Stone

There is an old saw in political science that difficult conditions become problems only when people come to see them as amenable to human action. Until then, difficulties remain embedded in the realm of nature, accident, and fate -a realm where there is no choice about what happens to us. The conversion of difficulties into problems is said to be the sine qua non of political rebellion, legal disputes, interest-group mobilization, and of moving policy problems onto the public agenda.' This article is about how situations come to be seen as caused by human actions and amenable to human intervention. Despite the acknowledged importance of this phenomenon as a precursor to political participation and to agenda setting, there is little systematic inquiry about it in the political science literature. For the most part, the question is dealt with under the rubric of agenda setting, even though the transformation of difficulties into problems takes place in something of a black box prior to agenda formation. Three strands of thinking in the agenda literature contribute indirectly to an understanding of this topic. One strand focuses on the identity and characteristics of political actors -leaders, interest groups, professionals, breaucrats. It looks at the actors' attitudes, resources, and opportunities

R. Snyder

Drude Dahlerup

T. Bolsen, R. Palm

Science is frequently used and distorted to advance political, economic, or cultural agendas. The politicization of science can limit the positive impacts that scientific advances can offer when people reject sound and beneficial scientific advice. Politicization has undoubtedly contributed to hesitancy toward uptake of the COVID-19 vaccine. It is urgent for scientists and clinicians to better understand: (1) the roots of politicization as related to COVID-19 vaccines; (2) the factors that influence people's receptivity to scientific misinformation in politicized contexts; and (3) how to combat the politicization of science to increase the use of life-saving vaccines. This chapter explores these issues in the context of COVID-19 vaccine resistance in the United States. After briefly describing the development of the vaccines, we describe the ways in which the disease itself became politicized because of statements by political leaders and also by media accounts including social media. We then review the politicization of the vaccine at both national and international scales, variability in public acceptance of the vaccines in the United States, and response to the emergence of variants. The next section summarizes social science findings on overcoming vaccine resistance, and the concluding section outlines some of the lessons of the politicization of the disease and the vaccine for health practitioners and life scientists.

Oleg Smirnov

Large language models (LLMs) are increasingly deployed as analytical tools across multilingual contexts, yet their outputs may carry systematic biases conditioned by the language of the prompt. This study presents an experimental comparison of LLM-generated political analyses of a Ukrainian civil society document, using semantically equivalent prompts in Russian and Ukrainian. Despite identical source material and parallel query structures, the resulting analyses varied substantially in rhetorical positioning, ideological orientation, and interpretive conclusions. The Russian-language output echoed narratives common in Russian state discourse, characterizing civil society actors as illegitimate elites undermining democratic mandates. The Ukrainian-language output adopted vocabulary characteristic of Western liberal-democratic political science, treating the same actors as legitimate stakeholders within democratic contestation. These findings demonstrate that prompt language alone can produce systematically different ideological orientations from identical models analyzing identical content, with significant implications for AI deployment in polarized information environments, cross-lingual research applications, and the governance of AI systems in multilingual societies.

J. Uscinski, A. Enders, Michelle I. Seelig et al.

Contemporary political ills at the mass behavior level (e.g., outgroup aggression, conspiracy theories) are often attributed to increasing polarization and partisan tribalism. We theorize that many such problems are less the product of left-right orientations than an orthogonal “anti-establishment” dimension of opinion dominated by conspiracy, populist, and Manichean orientations. Using two national surveys from 2019 and 2020, we find that this dimension of opinion is correlated with several antisocial psychological traits, the acceptance of political violence, and time spent on extremist social media platforms. It is also related to support for populist candidates, such as Trump and Sanders, and beliefs in misinformation and conspiracy theories. While many inherently view politics as a conflict between left and right, others see it as a battle between “the people” and a corrupt establishment. Our findings demonstrate an urgent need to expand the traditional conceptualization of mass opinion beyond familiar left-right identities and affective orientations. Verification Materials: The data, code, and any additional materials required to replicate all analyses in this article are available on the American Journal of Political Science Dataverse within the Harvard Dataverse Network, at: https: //doi.org/10.7910/DVN/YFPQJH Seeming increases in toxic political rhetoric, misinformation, and ideologically motivated violence have led pundits, politicians, and the public to become increasingly concerned about the health of contemporary American democracy. Journalists characterize the political landscape as rife with extremism, conspiracy theories, and mutual animosity, where civil unrest predominates and shared facts are a luxury of the past (e.g., Wang 2016). Even scholars, who typically take the long view, have sounded alarms (Carey et al. 2019; Levitsky and Ziblatt 2018; Runciman 2018). Who or what is to blame? Partisan tribalism and ideological extremism make attractive culprits, especially given the wealth of supportive evidence for this perspective scholars have amassed. Polarization has increased among the public, partisan and ideological identities have closely aligned, and hostility toward political outgroups has intensified Joseph E. Uscinski is Associate Professor, 1300 Campo Sano Blvd., Department of Political Science, University of Miami, Coral Gables, FL 33146 (uscinski@miami.edu). Adam M. Enders is Assistant Professor, Ford Hall, Department of Political Science, University of Louisville, Louisville, KY 40292 (adam.enders@louisville.edu). Michelle I. Seelig is Associate Professor, WCB 3019, Department of Cinema and Interactive Media, University of Miami, Coral Gables, FL 33146 (mseelig@miami.edu). Casey A. Klofstad is Professor, 1300 Campo Sano Blvd., Department of Political Science, University of Miami, Coral Gables, FL 33146 (c.klofstad@miami.edu). John R. Funchion is Associate Professor, 1252 Memorial Drive, Department of English, University of Miami, Coral Gables, FL 33146 (jfunchion@miami.edu). Caleb Everett is Professor, P.O. Box 248106, Department of Anthropology, University of Miami, Coral Gables, FL 33146 (caleb@miami.edu). Stefan Wuchty is Associate Professor, P.O. Box 248154, Department of Computer Science and Miami Institute of Data Science and Computing, University of Miami, Coral Gables, FL 33146 (s.wuchty@miami.edu). Kamal Premaratne is Professor, 1251 Memorial Drive, Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33146 (kamal@miami.edu). Manohar N. Murthi is Associate Professor, 1251 Memorial Drive, Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33146 (mmurthi@miami.edu). The ordering of the two lead authors’ names reflects a principle of rotation. Financial support was provided by the University of Miami U-Link initiative. We wish to thank Miles Armaly, as well as three anonymous reviewers, for their helpful comments. American Journal of Political Science, Vol. 00, No. 0, XXXX 2021, Pp. 1–19 ©2021, Midwest Political Science Association DOI: 10.1111/ajps.12616

Kanchan Chandra

Ramon Odebunmi

The Russo-Ukraine and Palestine-Israeli conflicts are one of the most devastating geo-political conflicts in the 21st century. From human rights perspective, the alleged violations of international law in the ongoing war in Ukraine and Palestine respectively are not only considered as abhorrent, but also raises significant concerns about the legitimacy of Russia and Israel’s aggression against Ukraine and Palestine respectively. The laws of war are means to an end to achieve legitimacy by showing respect for rule of law and abiding by universal ethical and moral principles. This paper therefore argues that Russia and Israel’s use of force against Ukraine and Palestine respectively violates international humanitarian law or law of wars on the basis of Article 2(4) and Article 51 of the UN Charter. This paper seeks to determine the legitimacy and illegitimacy of the military aggression of Russia and Israel in the context of international humanitarian law. To achieve this objective, the paper adopts the qualitative method of inquiry. This paper adopts the Just War theory to strengthen the argument of this paper by interrogating the legitimacy and illegitimacy of Russia and Israel’s use of force against Ukraine and Palestine as well as the conduct of the war. The paper concludes that Russia and Israel’s use of military force negates the universal principle of morality and ethics as their actions seriously negates the provisions of Article 2(4) and Article 51 of the UN charter. Israel’s use of force in Gaza is disproportionate, while Russia’s conduct of the war violates the prohibition of the use of force as provided by international humanitarian law.

Matous Volf, Jakub Simko

This paper addresses the challenge of automatically classifying text according to political leaning and politicalness using transformer models. We compose a comprehensive overview of existing datasets and models for these tasks, finding that current approaches create siloed solutions that perform poorly on out-of-distribution texts. To address this limitation, we compile a diverse dataset by combining 12 datasets for political leaning classification and creating a new dataset for politicalness by extending 18 existing datasets with the appropriate label. Through extensive benchmarking with leave-one-in and leave-one-out methodologies, we evaluate the performance of existing models and train new ones with enhanced generalization capabilities.

Melissa Costan, Kasim Costan, Anna Weißbach et al.

The gap between theory and practice is well-documented in educational research. Physics teachers' willingness to apply research findings in practice may be influenced by a sceptical attitude towards science education research. This study explores physics teachers' perspectives on science education research, with a particular focus on potential scepticism towards the discipline. A two-step mixed-methods approach was employed: (1) Interviews with a purposeful sample of 13 experienced physics teachers for a first exploration of attitudes towards physics education research, and (2) a quantitative survey of 174 physics teachers to examine, among other aspects, the previously observed attitudes in a larger sample and to identify teacher profiles using latent profile analysis. The interview study revealed both sceptical and non-sceptical attitudes towards physics education research, including some that fundamentally questioned its practical value. Based on the survey data and latent profile analysis, four distinct teacher profiles differing in their level of scepticism towards science education research were identified. While one profile is highly sceptical, the other three exhibit a mix of sceptical and supportive attitudes. Thus, physics teachers are not generally sceptical. However, the cooperation between research and practice is perceived as unproductive by most teachers.

Gabriela Juncosa, Taha Yasseri, Julia Koltai et al.

Quantifying how individuals react to social influence is crucial for tackling collective political behavior online. While many studies of opinion in public forums focus on social feedback, they often overlook the potential for human interactions to result in self-censorship. Here, we investigate political deliberation in online spaces by exploring the hypothesis that individuals may refrain from expressing minority opinions publicly due to being exposed to toxic behavior. Analyzing conversations under YouTube videos from six prominent US news outlets around the 2020 US presidential elections, we observe patterns of self-censorship signaling the influence of peer toxicity on users' behavior. Using hidden Markov models, we identify a latent state consistent with toxicity-driven silence. Such state is characterized by reduced user activity and a higher likelihood of posting toxic content, indicating an environment where extreme and antisocial behaviors thrive. Our findings offer insights into the intricacies of online political deliberation and emphasize the importance of considering self-censorship dynamics to properly characterize ideological polarization in digital spheres.

Jan Batzner, Volker Stocker, Stefan Schmid et al.

Large language models (LLMs) are increasingly shaping citizens' information ecosystems. Products incorporating LLMs, such as chatbots and AI Companions, are now widely used for decision support and information retrieval, including in sensitive domains, raising concerns about hidden biases and growing potential to shape individual decisions and public opinion. This paper introduces GermanPartiesQA, a benchmark of 418 political statements from German Voting Advice Applications across 11 elections to evaluate six commercial LLMs. We evaluate their political alignment based on role-playing experiments with political personas. Our evaluation reveals three specific findings: (1) Factual limitations: LLMs show limited ability to accurately generate factual party positions, particularly for centrist parties. (2) Model-specific ideological alignment: We identify consistent alignment patterns and the degree of political steerability for each model across temperature settings and experiments. (3) Claim of sycophancy: While models adjust to political personas during role-play, we find this reflects persona-based steerability rather than the increasingly popular, yet contested concept of sycophancy. Our study contributes to evaluating the political alignment of closed-source LLMs that are increasingly embedded in electoral decision support tools and AI Companion chatbots.

Yu Wang, Wen Qu, Xin Ye

Political scientists often grapple with data scarcity in text classification. Recently, fine-tuned BERT models and their variants have gained traction as effective solutions to address this issue. In this study, we investigate the potential of GPT-based models combined with prompt engineering as a viable alternative. We conduct a series of experiments across various classification tasks, differing in the number of classes and complexity, to evaluate the effectiveness of BERT-based versus GPT-based models in low-data scenarios. Our findings indicate that while zero-shot and few-shot learning with GPT models provide reasonable performance and are well-suited for early-stage research exploration, they generally fall short - or, at best, match - the performance of BERT fine-tuning, particularly as the training set reaches a substantial size (e.g., 1,000 samples). We conclude by comparing these approaches in terms of performance, ease of use, and cost, providing practical guidance for researchers facing data limitations. Our results are particularly relevant for those engaged in quantitative text analysis in low-resource settings or with limited labeled data.

L. Rheault, Christopher Cochrane

Word embeddings, the coefficients from neural network models predicting the use of words in context, have now become inescapable in applications involving natural language processing. Despite a few studies in political science, the potential of this methodology for the analysis of political texts has yet to be fully uncovered. This paper introduces models of word embeddings augmented with political metadata and trained on large-scale parliamentary corpora from Britain, Canada, and the United States. We fit these models with indicator variables of the party affiliation of members of parliament, which we refer to as party embeddings. We illustrate how these embeddings can be used to produce scaling estimates of ideological placement and other quantities of interest for political research. To validate the methodology, we assess our results against indicators from the Comparative Manifestos Project, surveys of experts, and measures based on roll-call votes. Our findings suggest that party embeddings are successful at capturing latent concepts such as ideology, and the approach provides researchers with an integrated framework for studying political language.

L. Stokes

S. Tarrow

Reysa Anggreyani, Suranto

This Research explores the social media used to advocate the issues in Surakarta city, in this case, using the social media specific to the Twitter data of the mayor of Surakarta, Gibran Rakabuming Raka (@gibran_tweet). This Research uses Qualitative approach and analysis data with Qualitative Data Analysis Software (Q-DAS). This Research founds that First, The social media content dichotomy of account Gibran Rakabuming Raka as Mayor of Surakarta's dominance of social media as part of reports tolls shows that social media can access everyone to report about the social infrastructure and other problems in Surakarta City. Second, the activity of the account of Gibran Rakbuming Raka focuses only on Surakarta city content. Third, the narration dominance with words related to the location Surakarta or Solo, indicated that the content of Gibran Rakabuming Raka as mayor of Surakata or Solo refers to the specific area in Solo or Surakarta, shows that the content dominance for local content of solo or Surakarta.