Hasil untuk "Political science"

F. Brandt, Vincent Conitzer, U. Endriss et al.

The rapidly growing field of computational social choice, at the intersection of computer science and economics, deals with the computational aspects of collective decision making. This handbook, written by thirty-six prominent members of the computational social choice community, covers the field comprehensively. Chapters devoted to each of the field's major themes offer detailed introductions. Topics include voting theory (such as the computational complexity of winner determination and manipulation in elections), fair allocation (such as algorithms for dividing divisible and indivisible goods), coalition formation (such as matching and hedonic games), and many more. Graduate students, researchers, and professionals in computer science, economics, mathematics, political science, and philosophy will benefit from this accessible and self-contained book.

Alberto Abadie, A. Diamond, Jens Hainmueller

Jens Hainmueller, D. Hopkins, Teppei Yamamoto

W. Adger, S. Dessai, M. Goulden et al.

R. Wodak, R. D. Cillia, M. Reisigl et al.

How do we construct national identities in discourse? Which topics, which discursive strategies and which linguistic devices are employed to construct national sameness and uniqueness on the one hand, and differences to other national collectives on the other hand? The Discursive Construction of National Identity analyses discourses of national identity in Europe with particular attention to Austria. In the tradition of critical discourse analysis, the authors analyse current and on-going transformations in the self-and other definition of national identities using an innovative interdisciplinary approach which combines discourse-historical theory and methodology and political science perspectives. Thus, the rhetorical promotion of national identification and the discursive construction and reproduction of national difference on public, semi-public and semi-private levels within a nation state are analysed in much detail and illustrated with a huge amount of examples taken from many genres (speeches, focus-groups, interviews, media, and so forth). In addition to the critical discourse analysis of multiple genres accompanying various commemorative and celebratory events in 1995, this extended and revised edition is able to draw comparisons with similar events in 2005. The impact of socio-political changes in Austria and in the European Union is also made transparent in the attempts of constructing hegemonic national identities. Key Features: *Discourse-historical approach. *Interdisciplinarity (cultural studies, discourse analysis, history, political science). *Multi-method, multi-genre. *Qualitative case studies.

B. Bass, R. Bass

J. Gold, M. Shadlen

T. Rhodes

R. Alford, Gary King, R. Keohane et al.

W. Powell, Paul DiMaggio

J. Gerring

M. Lamont, Virág Molnár

R. Putnam

L. Althusser, Benjamin Brewster

J. Gerring

P. S. Hart, E. Nisbet

Scott Armstrong

Eva Lövbrand, S. Beck, J. Chilvers et al.

Stefano Civelli, Pietro Bernardelle, Gianluca Demartini

While pretraining language models with politically diverse content has been shown to improve downstream task fairness, such approaches require significant computational resources often inaccessible to many researchers and organizations. Recent work has established that persona-based prompting can introduce political diversity in model outputs without additional training. However, it remains unclear whether such prompting strategies can achieve results comparable to political pretraining for downstream tasks. We investigate this question using persona-based prompting strategies in multimodal hate-speech detection tasks, specifically focusing on hate speech in memes. Our analysis reveals that when mapping personas onto a political compass and measuring persona agreement, inherent political positioning has surprisingly little correlation with classification decisions. Notably, this lack of correlation persists even when personas are explicitly injected with stronger ideological descriptors. Our findings suggest that while LLMs can exhibit political biases in their responses to direct political questions, these biases may have less impact on practical classification tasks than previously assumed. This raises important questions about the necessity of computationally expensive political pretraining for achieving fair performance in downstream tasks.

Joseph R. Loffredo, Suyeol Yun

The applications of Large Language Models (LLMs) in political science are rapidly expanding. This paper demonstrates how LLMs, when augmented with predefined functions and specialized tools, can serve as dynamic agents capable of streamlining tasks such as data collection, preprocessing, and analysis. Central to this approach is agentic retrieval-augmented generation (Agentic RAG), which equips LLMs with action-calling capabilities for interaction with external knowledge bases. Beyond information retrieval, LLM agents may incorporate modular tools for tasks like document summarization, transcript coding, qualitative variable classification, and statistical modeling. To demonstrate the potential of this approach, we introduce CongressRA, an LLM agent designed to support scholars studying the U.S. Congress. Through this example, we highlight how LLM agents can reduce the costs of replicating, testing, and extending empirical research using the domain-specific data that drives the study of political institutions.