The rapid integration of generative artificial intelligence (AI) tools is transforming higher education. This article examines the pedagogical, ethical, and discipline-specific implications of AI use, arguing that political science is uniquely positioned to both utilize and critically interrogate these technologies. The paper highlights the opportunities and risks of AI, including its potential to democratize access and scaffold learning, as well as its capacity to reinforce bias, encourage superficial thinking, and complicate academic integrity. Practical strategies for integrating AI into political science education are proposed, including reflective assignments, simulationbased learning, and student-led evaluations. The study concludes that educators must move beyond reactive policies toward proactive pedagogical design.
Engaging in political discussions is crucial in democratic societies, yet many individuals remain politically disinclined due to various factors such as perceived knowledge gaps, conflict avoidance, or a sense of disconnection from the political system. In this paper, we explore the potential of personal narratives-short, first-person accounts emphasizing personal experiences-as a means to empower these individuals to participate in online political discussions. Using a text classifier that identifies personal narratives, we conducted a large-scale computational analysis to evaluate the relationship between the use of personal narratives and participation in political discussions on Reddit. We find that politically disinclined individuals (PDIs) are more likely to use personal narratives than more politically active users. Personal narratives are more likely to attract and retain politically disinclined individuals in political discussions than other comments. Importantly, personal narratives posted by politically disinclined individuals are received more positively than their other comments in political communities. These results emphasize the value of personal narratives in promoting inclusive political discourse.
This paper investigates how political campaigns engaged UK football fan communities on Twitter in the aftermath of the Brexit Referendum (2016-2017). Football fandom, with its strong collective identities and tribal behaviours, offers fertile ground for political influence. Combining social network and content analysis, we examine how political discourse became embedded in football conversations. We show that a wide range of actors -- including parties, media, activist groups, and pseudonymous influencers -- mobilised support, provoked reactions, and shaped opinion within these communities. Through case studies of hashtag hijacking, embedded activism, and political "megaphones", we illustrate how campaigns leveraged fan cultures to amplify political messages. Our findings highlight mechanisms of political influence in ostensibly non-political online spaces and point toward the development of a broader framework in future work.
Grace Wolf-Chase, Charles Kerton, Kathryn Devine
et al.
We review participatory science programs that have contributed to the understanding of star formation. The Milky Way Project (MWP), one of the earliest participatory science projects launched on the Zooniverse platform, produced the largest catalog of ``bubbles'' associated with feedback from hot young stars to date, and enabled the identification of a new class of compact star-forming regions (SFRs) known as ``yellowballs'' (YBs). The analysis of YBs through their infrared colors and catalog cross-matching led to discovering that YBs are compact photodissociation regions generated by intermediate- and high-mass young stellar objects embedded in clumps that range in mass from 10 - 10,000 solar masses and luminosity from 10 - 1,000,000 solar luminosities. The MIRION catalog, assembled from 6176 YBs identified by citizen scientists, increases the number of candidate intermediate-mass SFRs by nearly two orders of magnitude. Ongoing work utilizing data from the Spitzer, Herschel and WISE missions involves analyzing infrared color trends to predict physical properties and ages of YB environments. Methods include applying summary statistics to histograms and color-color plots as well as SED fitting. Students in introductory astronomy classes contribute toward continued efforts refining photometric measurements of YBs while learning fundamental concepts in astronomy through a classroom-based participatory science experience, the PERYSCOPE project. We also describe an initiative that engaged seminaries, family groups, and interfaith communities in a wide variety of science projects on the Zooniverse platform. This initiative produced important guidance on attracting audiences that are underserved, underrepresented, or apprehensive about science.
Juan David Gutiérrez, Sarah Maria Muñoz-Cadena, María Carolina Corcione
Este artículo analiza cómo participan e inciden las agencias reguladoras en el ciclo de las políticas públicas en Colombia. La investigación desarrolla un caso de estudio comparativo de la Superintendencia de Industria y Comercio (SIC) y la Comisión de Regulación de Comunicaciones (CRC). El artículo estudia cómo la SIC y la CRC participan e inciden en procesos de formación de la agenda, formulación, implementación y evaluación de políticas públicas. El caso de estudio comparado se realizó a partir de tres tipos de fuentes primarias documentales: instrumentos jurídicos, documentos producidos por las agencias y archivos de prensa. En resumen, reportamos tres hallazgos: (i) las dos agencias comparadas participan activa y directamente en procesos de agendamiento e implementación de política pública; (ii) sus esfuerzos en materia de evaluación de políticas es relativamente bajo; y, (iii) su rol en materia de formulación de políticas públicas diverge: mientras que la CRC diseña y adopta periódicamente instrumentos de política pública, la participación de la SIC en la formulación es más indirecta pues se enfoca en analizar proyectos regulatorios de otras entidades públicas para realizar recomendaciones cuando considera que puede haber afectaciones a la competencia.
Law, Law in general. Comparative and uniform law. Jurisprudence
The article introduces a new concept designed to describe the social and sociotechnical processes that were triggered by digital transformation and, in their turn, resulted in Industry 5.0. The author described and modelled egocentric, communicative, convergent, and cause-and-effect rational-semantic networks, in which rationality relies on the semantic model of the system and forms some rational concern for its values and meanings to be implemented in behavior. The theoretical side of the research could be represented as a pyramid of conceptual levels that concentrate from philosophy and general science to particular research with statistical, network, and other methods. The author introduced the term of rational-semantic system to study the network contexts of behavior, including that of social network users. The term was also applied to the phenomena and effects of network interaction, e.g. the legitimacy of network power and its effect on user behavior patterns. The methodological character of this research allows for a broader study of social and political networks. The network context revealed some congruence issues, i.e., compatibility of rational-semantic systems. The author believes that eventually all systems can be combined into a single whole.
I report here a comprehensive analysis about the political preferences embedded in Large Language Models (LLMs). Namely, I administer 11 political orientation tests, designed to identify the political preferences of the test taker, to 24 state-of-the-art conversational LLMs, both closed and open source. When probed with questions/statements with political connotations, most conversational LLMs tend to generate responses that are diagnosed by most political test instruments as manifesting preferences for left-of-center viewpoints. This does not appear to be the case for five additional base (i.e. foundation) models upon which LLMs optimized for conversation with humans are built. However, the weak performance of the base models at coherently answering the tests' questions makes this subset of results inconclusive. Finally, I demonstrate that LLMs can be steered towards specific locations in the political spectrum through Supervised Fine-Tuning (SFT) with only modest amounts of politically aligned data, suggesting SFT's potential to embed political orientation in LLMs. With LLMs beginning to partially displace traditional information sources like search engines and Wikipedia, the societal implications of political biases embedded in LLMs are substantial.
Predicting roll call votes through modeling political actors has emerged as a focus in quantitative political science and computer science. Widely used embedding-based methods generate vectors for legislators from diverse data sets to predict legislative behaviors. However, these methods often contend with challenges such as the need for manually predefined features, reliance on extensive training data, and a lack of interpretability. Achieving more interpretable predictions under flexible conditions remains an unresolved issue. This paper introduces the Political Actor Agent (PAA), a novel agent-based framework that utilizes Large Language Models to overcome these limitations. By employing role-playing architectures and simulating legislative system, PAA provides a scalable and interpretable paradigm for predicting roll-call votes. Our approach not only enhances the accuracy of predictions but also offers multi-view, human-understandable decision reasoning, providing new insights into political actor behaviors. We conducted comprehensive experiments using voting records from the 117-118th U.S. House of Representatives, validating the superior performance and interpretability of PAA. This study not only demonstrates PAA's effectiveness but also its potential in political science research.
Hubert Plisiecki, Paweł Lenartowicz, Maria Flakus
et al.
This paper investigates the presence of political bias in emotion inference models used for sentiment analysis (SA) in social science research. Machine learning models often reflect biases in their training data, impacting the validity of their outcomes. While previous research has highlighted gender and race biases, our study focuses on political bias - an underexplored yet pervasive issue that can skew the interpretation of text data across a wide array of studies. We conducted a bias audit on a Polish sentiment analysis model developed in our lab. By analyzing valence predictions for names and sentences involving Polish politicians, we uncovered systematic differences influenced by political affiliations. Our findings indicate that annotations by human raters propagate political biases into the model's predictions. To mitigate this, we pruned the training dataset of texts mentioning these politicians and observed a reduction in bias, though not its complete elimination. Given the significant implications of political bias in SA, our study emphasizes caution in employing these models for social science research. We recommend a critical examination of SA results and propose using lexicon-based systems as a more ideologically neutral alternative. This paper underscores the necessity for ongoing scrutiny and methodological adjustments to ensure the reliability and impartiality of the use of machine learning in academic and applied contexts.
This study examines China-Kenya financial engagement in the context of the discovery of natural resources in Kenya. Based on the analysis of the outcomes of financial instruments, we use trade dependence hypotheses to determine whether financial outcomes are influenced by perceptions of China’s dependence on Kenya’s market and China’s quest to access discovered natural resources. It is argued that between 2006 and 2011, China’s dependence on Kenya’s market did not influence perceptions of negotiators leading to “unfavourable” financial outcomes, however, this changed in the context of China’s quest to access discovered natural resources between 2012 and 2015, thus leading to “favourable” financial outcomes. The significance of the findings is that China provided more liberalized credit lines to the Kenyan government after Chinese firms began to express more interest in natural resource extraction.
Enforcement and management scholars alike expect that countries participating in an international agreement will more likely achieve predetermined targets than nonparticipating countries will. The management school ascribes this expected association to a constraining effect of the treaty; the enforcement school ascribes it to a screening effect. If the latter conjecture is correct, the association between participation and target achievement should significantly weaken (or even vanish) when controlling for targets' ambition level and other confounding factors. We test this hypothesis on a new dataset comprising three protocols under the Convention on Long-Range Transboundary Air Pollution (CLRTAP). Our results suggest that the positive association between participation and target achievement is robust to controlling for confounding factors; hence, our data suggests that these CLRTAP protocols have indeed constrained participating states.
Emilio Vital Brazil, Eduardo Soares, Lucas Villa Real
et al.
Data is a critical element in any discovery process. In the last decades, we observed exponential growth in the volume of available data and the technology to manipulate it. However, data is only practical when one can structure it for a well-defined task. For instance, we need a corpus of text broken into sentences to train a natural language machine-learning model. In this work, we will use the token \textit{dataset} to designate a structured set of data built to perform a well-defined task. Moreover, the dataset will be used in most cases as a blueprint of an entity that at any moment can be stored as a table. Specifically, in science, each area has unique forms to organize, gather and handle its datasets. We believe that datasets must be a first-class entity in any knowledge-intensive process, and all workflows should have exceptional attention to datasets' lifecycle, from their gathering to uses and evolution. We advocate that science and engineering discovery processes are extreme instances of the need for such organization on datasets, claiming for new approaches and tooling. Furthermore, these requirements are more evident when the discovery workflow uses artificial intelligence methods to empower the subject-matter expert. In this work, we discuss an approach to bringing datasets as a critical entity in the discovery process in science. We illustrate some concepts using material discovery as a use case. We chose this domain because it leverages many significant problems that can be generalized to other science fields.
While sanctions in political and economic areas are now part of the standard repertoire of Western countries (not always endorsed by UN mandates), sanctions in science and culture in general are new. Historically, fundamental research as conducted at international research centers such as CERN has long been seen as a driver for peace, and the Science4Peace idea has been celebrated for decades. However, much changed with the war against Ukraine, and most Western science organizations put scientific cooperation with Russia and Belarus on hold immediately after the start of the war in 2022. In addition, common publications and participation in conferences were banned by some institutions, going against the ideal of free scientific exchange and communication. These and other points were the topics of an international virtual panel discussion organized by the Science4Peace Forum together with the "Natural Scientists Initiative - Responsibility for Peace and Sustainability" (NatWiss e.V.) in Germany and the journal "Wissenschaft und Frieden" (W&F) (see the Figure). Fellows from the Hamburg Institute for Peace Research and Security Policy (IFSH), scientists collaborating with the large physics research institutes DESY and CERN, as well as from climate and futures researchers were represented on the panel. In this Dossier we document the panel discussion, and give additional perspectives. The authors of the individual sections present their personal reflections, which should not be taken as implying that they are endorsed by the Science4Peace Forum or any other organizations. It is regrettable that some colleagues who expressed support for this document felt that it would be unwise for them to co-sign it.
Angelo Salatino, Simone Angioni, Francesco Osborne
et al.
Understanding the relationship between the composition of a research team and the potential impact of their research papers is crucial as it can steer the development of new science policies for improving the research enterprise. Numerous studies assess how the characteristics and diversity of research teams can influence their performance across several dimensions: ethnicity, internationality, size, and others. In this paper, we explore the impact of diversity in terms of the authors' expertise. To this purpose, we retrieved 114K papers in the field of Computer Science and analysed how the diversity of research fields within a research team relates to the number of citations their papers received in the upcoming 5 years. The results show that two different metrics we defined, reflecting the diversity of expertise, are significantly associated with the number of citations. This suggests that, at least in Computer Science, diversity of expertise is key to scientific impact.
Software plays a central role in scientific discovery. Improving how we develop and use software for research can have both broad and deep impacts on a spectrum of challenges and opportunities society faces today. The emergence of Research Software Engineer (RSE) as a role correlates with the growing complexity of scientific challenges and diversity of software team skills. In this paper, we describe research software science (RSS), an idea related to RSE, and particularly suited to research software teams. RSS promotes the use of scientific methodologies to explore and establish broadly applicable knowledge. Using RSS, we can pursue sustainable, repeatable, and reproducible software improvements that positively impact research software toward improved scientific discovery.
Modeling the ideological perspectives of political actors is an essential task in computational political science with applications in many downstream tasks. Existing approaches are generally limited to textual data and voting records, while they neglect the rich social context and valuable expert knowledge for holistic evaluation. In this paper, we propose a representation learning framework of political actors that jointly leverages social context and expert knowledge. Specifically, we retrieve and extract factual statements about legislators to leverage social context information. We then construct a heterogeneous information network to incorporate social context and use relational graph neural networks to learn legislator representations. Finally, we train our model with three objectives to align representation learning with expert knowledge, model ideological stance consistency, and simulate the echo chamber phenomenon. Extensive experiments demonstrate that our learned representations successfully advance the state-of-the-art in three downstream tasks. Further analysis proves the correlation between learned legislator representations and various socio-political factors, as well as bearing out the necessity of social context and expert knowledge in modeling political actors.
Guilherme Asai, Carlos Alberto Piacenti, Angelo Costa Gurgel
Objetivo: este trabalho teve como objetivo investigar o impacto da oscilação do custo do frete para os produtos agropecuários brasileiros.
Método: trata-se de uma pesquisa descritiva de natureza quantitativa, focado na análise de cenário por meio da modelagem em equilíbrio geral computável para as cinco macrorregiões brasileiras.
Principais resultados: como resultado da pesquisa indica-se: (i) a importação e a exportação sofreram impacto causado pela variação do preço do frete, aumentando em todas as macrorregiões brasileiras; (ii) o aumento das importações e exportações é um indicativo no movimento dos fluxos comerciais e traz benefícios para a economia do país, favorecendo a troca e a economia regional e (iii) as regiões com menor custo de frete, apresentam ganhos de competitividade para produtos agropecuários.
Relevância/originalidade: trabalho contribui com uma agenda de pesquisa que envolve setores importantes da economia brasileira – agropecuária e transportes – e como estão relacionados entre si, fornecendo uma visão de como o custo de frete influência no comércio dos produtos agropecuários no Brasil.
Contribuições teóricas/metodológicas: o trabalho apresenta uma abordagem diferente de métodos mais tradicionais, por ECG, criando uma alternativa para análise de como o custo de transporte impacta no comércio inter-regional.
Cindy L. Young, Michael H. Wong, Kunio M. Sayanagi
et al.
The National Academy Committee on Astrobiology and Planetary Science (CAPS) made a recommendation to study a large/medium-class dedicated space telescope for planetary science, going beyond the Discovery-class dedicated planetary space telescope endorsed in Visions and Voyages. Such a telescope would observe targets across the entire solar system, engaging a broad spectrum of the science community. It would ensure that the high-resolution, high-sensitivity observations of the solar system in visible and UV wavelengths revolutionized by the Hubble Space Telescope (HST) could be extended. A dedicated telescope for solar system science would: (a) transform our understanding of time-dependent phenomena in our solar system that cannot be studied currently under programs to observe and visit new targets and (b) enable a comprehensive survey and spectral characterization of minor bodies across the solar system, which requires a large time allocation not supported by existing facilities. The time-domain phenomena to be explored are critically reliant on high spatial resolution UV-visible observations. This paper presents science themes and key questions that require a long-lasting space telescope dedicated to planetary science that can capture high-quality, consistent data at the required cadences that are free from effects of the terrestrial atmosphere and differences across observing facilities. Such a telescope would have excellent synergy with astrophysical facilities by placing planetary discoveries made by astrophysics assets in temporal context, as well as triggering detailed follow-up observations using larger telescopes. The telescope would support future missions to the Ice Giants, Ocean Worlds, and minor bodies across the solar system by placing the results of such targeted missions in the context of longer records of temporal activities and larger sample populations.
Aprajita Verma, Thomas Collett, Graham P. Smith
et al.
Strong gravitational lensing enables a wide range of science: probing cosmography; testing dark matter models; understanding galaxy evolution; and magnifying the faint, small and distant Universe. However to date exploiting strong lensing as a tool for these numerous cosmological and astrophysical applications has been severely hampered by limited sample sized. LSST will drive studies of strongly lensed galaxies, galaxy groups and galaxy clusters into the statistical age. Time variable lensing events, e.g. measuring cosmological time delays from strongly lensed supernovae and quasars, place the strongest constraints on LSST's observing strategy and have been considered in the DESC observing strategy white papers. Here we focus on aspects of `static' lens discovery that will be affected by the observing strategy. In summary, we advocate (1) ensuring comparable (sub-arcsecond) seeing in the g-band as in r and i to facilitate discovery of gravitational lenses, and (2) initially surveying the entire observable extragalactic sky as rapidly as possible to enable early science spanning a broad range of static and transient interests.