Hasil untuk "Political Science"

Șerban. Henrieta

In political science today, the notions of “governance“ and “good governance“ compete as synonyms. The negative connotations of the first term (from the sphere of significance of the idea of leadership, or also care for one or more minors or immatures of any age, apprentices, etc.) are clear. That is why the preference for the phrase “good governance“ is justified, it is democratic, it is even a correct ethical preference. Extremely suggestive of “good governance“ is the image of the good steering of a ship at sea (κυβερνάω), protecting it from rocks and any other dangers and ensuring it a maximum advance with as little consumption as possible, an image also consistent with the etymology involved for governance that we choose to translate by the phrase “good governance“. The complexity of the topic is addressed via a multitude of aspects and directions of analysis including, but not limited to, the differences between “digitalization“ and “digitization“ , the Council of Europe view on digital good governance, the recommendations made by OECD in the Romanian situation in what concerns digitalization, the case of “Ion“, the IA governance adviser etc. The study concludes that digital good governance should ensure and not challenge in any way the defining characteristics of good governance: participation, rule of law, transparency, responsiveness, the orientation towards consensus social inclusion efficiency and efficacity, accountability, the guaranty of human rights and good practices. The governing of the digital space and of the algorithms should prevail over the governance of the citizens by the algorithms.

Xuenan Cao, Wai Kei Chung, Ye Zhao et al.

Ask your chatbot to impersonate an expert from Russia and an expert from US and query it on Chinese politics. How might the outputs differ? Or, to prepare ourselves for the worse, how might they converge? Scholars have raised concerns LLM based applications can homogenize cultures and flatten perspectives. But exactly how much does LLM generated outputs converge despite explicit different role assignment? This study provides empirical evidence to the above question. The critique centres on pretrained models regurgitating ossified political jargons used in the Western world when speaking about China, Iran, Russian, and US politics, despite changes in these countries happening daily or hourly. The experiments combine role-prompting and similarity metrics. The results show that AI generated discourses from four models about Iran and China are the most homogeneous and unchanging across all four models, including OpenAI GPT, Google Gemini, Anthropic Claude, and DeepSeek, despite the prompted perspective change and the actual changes in real life. This study does not engage with history, politics, or literature as traditional disciplinary approaches would; instead, it takes cues from international and area studies and offers insight on the future trajectory of shifting political discourse in a digital space increasingly cannibalised by AI.

Thilo Hagendorff

The guiding principle of AI alignment is to train large language models (LLMs) to be harmless, helpful, and honest (HHH). At the same time, there are mounting concerns that LLMs exhibit a left-wing political bias. Yet, the commitment to AI alignment cannot be harmonized with the latter critique. In this article, I argue that intelligent systems that are trained to be harmless and honest must necessarily exhibit left-wing political bias. Normative assumptions underlying alignment objectives inherently concur with progressive moral frameworks and left-wing principles, emphasizing harm avoidance, inclusivity, fairness, and empirical truthfulness. Conversely, right-wing ideologies often conflict with alignment guidelines. Yet, research on political bias in LLMs is consistently framing its insights about left-leaning tendencies as a risk, as problematic, or concerning. This way, researchers are actively arguing against AI alignment, tacitly fostering the violation of HHH principles.

Joseph Abdou

This research aims at providing a mathematical model of the organization of the polity and its transformation. For that purpose we construct two categories named respectively Political Configuration and Political Foundation. Our construction depends on a couple of variables called the foundational pair. One variable, called the Base, consists of a finite number of members (agents), while the other, called the Ground, consists of a set of states that reflect all relevant interests/values/aspirations of the base members. An object of the Configuration, called p-formation, extends the notion of simplicial complex, and a morphism, which expresses the recomposition of the base, extends the notion of simplicial map. An object of the Foundation, called p-site, describes the profile of the polity, that is, how the states of the ground are intertwined between the agents. A morphism between political sites consists of a pair of maps, namely a Base map and a Ground map, satisfying appropriate conditions. Two functors relate the Foundation and the Configuration: the Knit which attributes to each p-site a p-formation and the Nerve which attributes to each p-site a simplicial complex. In the opposite direction a functor, called Canon, which attributes to any p-formation its canonical p-site, turns out to be in an appropriate sense the inverse of the Knit and the Nerve.

Innocentus Alhamis

Public policy shapes the economic landscape, influencing everything from corporate behavior to individual investment decisions. For Environmental, Social, and Governance (ESG) investors, these policy shifts can create opportunities and challenges as they navigate an ever-changing regulatory environment. The contrast between the Trump and Biden administrations offers a striking example of how differing political agendas can affect ESG investments. Trump's first term was marked by deregulation and policies favoring fossil fuels, which created an uncertain environment for sustainable investments. When Biden assumed office, his focus on climate action and clean energy reinvigorated the ESG sector, offering a more stable and supportive landscape for green investments. However, with Trump's return to power in his second term, these policies are being reversed again, leading to further volatility. This paper explores how such dramatic shifts in public policy influence economic strategies and directly impact ESG investors' decisions, forcing them to constantly reassess their portfolios in response to changing political climates.

Veronica Chatrath, Marcelo Lotif, Shaina Raza

Political misinformation poses significant challenges to democratic processes, shaping public opinion and trust in media. Manual fact-checking methods face issues of scalability and annotator bias, while machine learning models require large, costly labelled datasets. This study investigates the use of state-of-the-art large language models (LLMs) as reliable annotators for detecting political factuality in news articles. Using open-source LLMs, we create a politically diverse dataset, labelled for bias through LLM-generated annotations. These annotations are validated by human experts and further evaluated by LLM-based judges to assess the accuracy and reliability of the annotations. Our approach offers a scalable and robust alternative to traditional fact-checking, enhancing transparency and public trust in media.

Ehsan-Ul Haq, Haris Bin Zia, Reza Hadi Mogavi et al.

We share the largest dataset for the Pakistani Twittersphere consisting of over 49 million tweets, collected during one of the most politically active periods in the country. We collect the data after the deposition of the government by a No Confidence Vote in April 2022. This large-scale dataset can be used for several downstream tasks such as political bias, bots detection, trolling behavior, (dis)misinformation, and censorship related to Pakistani Twitter users. In addition, this dataset provides a large collection of tweets in Urdu and Roman Urdu that can be used for optimizing language processing tasks.

Kai-Cheng Yang, Filippo Menczer

Search engines increasingly leverage large language models (LLMs) to generate direct answers, and AI chatbots now access the Internet for fresh data. As information curators for billions of users, LLMs must assess the accuracy and reliability of different sources. This paper audits nine widely used LLMs from three leading providers -- OpenAI, Google, and Meta -- to evaluate their ability to discern credible and high-quality information sources from low-credibility ones. We find that while LLMs can rate most tested news outlets, larger models more frequently refuse to provide ratings due to insufficient information, whereas smaller models are more prone to making errors in their ratings. For sources where ratings are provided, LLMs exhibit a high level of agreement among themselves (average Spearman's $ρ= 0.79$), but their ratings align only moderately with human expert evaluations (average $ρ= 0.50$). Analyzing news sources with different political leanings in the US, we observe a liberal bias in credibility ratings yielded by all LLMs in default configurations. Additionally, assigning partisan roles to LLMs consistently induces strong politically congruent bias in their ratings. These findings have important implications for the use of LLMs in curating news and political information.

Sanyukta Deshpande, Ian G Ludden, Sheldon H Jacobson

Gerrymandering, the deliberate manipulation of electoral district boundaries for political advantage, is a persistent issue in U.S. redistricting cycles. This paper introduces and analyzes a new phenomenon, 'votemandering'- a strategic blend of gerrymandering and targeted political campaigning, devised to gain more seats by circumventing fairness measures. It leverages accurate demographic and socio-political data to influence voter decisions, bolstered by advancements in technology and data analytics, and executes better-informed redistricting. Votemandering is established as a Mixed Integer Program (MIP) that performs fairness-constrained gerrymandering over multiple election rounds, via unit-specific variables for campaigns. To combat votemandering, we present a computationally efficient heuristic for creating and testing district maps that more robustly preserve voter preferences. We analyze the influence of various redistricting constraints and parameters on votemandering efficacy. We explore the interconnectedness of gerrymandering, substantial campaign budgets, and strategic campaigning, illustrating their collective potential to generate biased electoral maps. A Wisconsin State Senate redistricting case study substantiates our findings on real data, demonstrating how major parties can secure additional seats through votemandering. Our findings underscore the practical implications of these manipulations, stressing the need for informed policy and regulation to safeguard democratic processes.

Siddhartha Bandyopadhyay, Amit K Chattopadhyay, Mandar Oak

We analyze conflict between a citizenry and an insurgent group over a fixed resource such as land. The citizenry has an elected leader who proposes a division such that, the lower the land ceded to the insurgents, the higher the cost of conflict. Leaders differ in ability and ideology such that the higher the leader's ability, the lower the cost of conflict, and the more hawkish the leader, the higher his utility from retaining land. We show that the conflict arises from the political process with re-election motives causing leaders to choose to cede too little land to signal their ability. We also show that when the rents of office are high, the political equilibrium and the second best diverge; in particular, the policy under the political equilibrium is more hawkish compared to the second best. When both ideology and ability are unknown, we provide a plausible condition under which the probability of re-election increases in the leader's hawkishness, thereby providing an explanation for why hawkish politicians may have a natural advantage under the electoral process.

Xiao Ao, Danae Sánchez Villegas, Daniel Preoţiuc-Pietro et al.

Parody is a figurative device used for mimicking entities for comedic or critical purposes. Parody is intentionally humorous and often involves sarcasm. This paper explores jointly modelling these figurative tropes with the goal of improving performance of political parody detection in tweets. To this end, we present a multi-encoder model that combines three parallel encoders to enrich parody-specific representations with humor and sarcasm information. Experiments on a publicly available data set of political parody tweets demonstrate that our approach outperforms previous state-of-the-art methods.

Victor Gabriel Menezes Menegassi, Naiara Sandi de Almeida Alcantara

The rise of conservative politicians with authoritarian attitudes, not only in Brazil, but in several countries around the world, may be part of the cultural backlash of a social group unhappy with recent cultural changes. These leaders who are not able to exercise democratic governance are in line with the increase in political intolerance. Therefore, our goal is precisely to connect the research agenda on adherence to democratic norms and tolerance, in order to verify the existence of this relationship in Brazil. For this, we use quantitative analysis te-chniques, through the empirical material provided by the Latin American Public Opinion Project. We start from the hypothesis that individuals who adhere more to democratic norms tend to be more tolerant towards minority groups. Among the results, we verified the existence of a highly significant relationship between the variables tested, that is, Democrats tend to be more tolerate.

Matthieu Burnay, Eva Pils

This paper offers a critical and historical analysis of the transformation of citizenship in China in a way that challenges both legal orientalism and the overall discourse on Chinese “characteristics” and “exceptionalism”. It aims to uncover how citizenship has been transformed “structurally” (Solinger 1999) as well as through “acts of citizenship” (Jakimow 2012). The paper will therefore not only look at how the One-Party State defines citizenship, uses it as an instrument of repression and population control, but also how citizens themselves can contribute to a new narrative on citizenship and driver of contestation in China. The paper will argue that the transformation of citizenship has contributed to the reinforcement of the fragmented and transnational nature of Chinese citizenship.

José María Baño León

La noción de plan general que contienen todas las leyes de las Comunidades Autónomas está obsoleta porque no responde a las necesidades públicas. El trabajo propugna una nueva visión jurídica de la planificación desligada de la asignación de derechos y acorde con las tendencias predominantes del derecho comparado.

Lucía Casado Casado

La aprobación de la Ley 39/2015, de 1 de octubre, del Procedimiento Administrativo Común de las Administraciones Públicas, supone la introducción de un nuevo trámite de consulta pública previa en el procedimiento de elaboración de normas con rango legal y reglamentario, regulado en su artículo 133. Este trámite, de carácter preceptivo, refuerza la participación ciudadana, al garantizarse en una fase muy temprana, previa al procedimiento normativo en sentido propio, cuando todavía no existe una propuesta redactada del proyecto o anteproyecto de ley o de reglamento y todas las opciones están abiertas. Sin embargo, nada dice este precepto sobre los efectos jurídicos que se anudan a la omisión –o práctica inadecuada– de la consulta pública previa, a pesar de ser ésta una cuestión de gran relevancia y que, en el fondo, determina la efectividad real de este trámite. Sobre esta cuestión versa este trabajo que, ante el silencio de la LPACAP, pretende aportar herramientas a los operadores jurídicos que ilustren sobre cuáles deben ser los efectos jurídicos de la omisión de este trámite en el procedimiento de elaboración de normas reglamentarias. Con este fin, se analizan tanto las diferentes interpretaciones ofrecidas por la doctrina como la jurisprudencia recaída hasta el momento.

Dean Kostantaras

The State of Nationalism (SoN) seeks to create a comprehensive guide to nationalism. This installment of SoN is dedicated to a review and annotated bibliography about 'Social Class'.

Abigail R. Azari, John B. Biersteker, Ryan M. Dewey et al.

Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets. Despite the increasing volume of planetary observations, our field has seen few applications of ML in comparison to other sciences. To support these methods, we propose ten recommendations for bolstering a data-rich future in planetary science.

Vishnu Viswanathan, Erwan Mazarico, Stephen Merkowitz et al.

The Lunar Laser Ranging (LLR) experiment has accumulated 50 years of range data of improving accuracy from ground stations to the laser retroreflector arrays (LRAs) on the lunar surface. The upcoming decade offers several opportunities to break new ground in data precision through the deployment of the next generation of single corner-cube lunar retroreflectors and active laser transponders. This is likely to expand the LLR station network. Lunar dynamical models and analysis tools have the potential to improve and fully exploit the long temporal baseline and precision allowed by millimetric LLR data. Some of the model limitations are outlined for future efforts. Differential observation techniques will help mitigate some of the primary limiting factors and reach unprecedented accuracy. Such observations and techniques may enable the detection of several subtle signatures required to understand the dynamics of the Earth-Moon system and the deep lunar interior. LLR model improvements would impact multi-disciplinary fields that include lunar and planetary science, Earth science, fundamental physics, celestial mechanics and ephemerides.

Matteo Cirillo

The time evolution of the response of electors to political life is analyzed for the Italian Republic over a 70 years span in which 18 political elections have taken place. The basis for the performed analyses are the official data available from the Italian Ministry of Interiors exposing the results of the political elections from 1948 until 2018. The attention is concentrated on parameters providing information on the responsivity of the electors to country political life. These parameters, expressed in adequate percentages, are the effective number of voters and the percentage of these expressing blank or spoilt ballots. The time dependence of these parameters, over the analyzed period, shows regularities, correlations and interesting peculiarities. The analysis concerns the results for both Chamber of Deputies and Chamber of Senators available, for each election, all along the 70 years time span that are those relative to people voting on the national territory.

Heidi B. Hammel, Stefanie N. Milam

Astrophysics facilities have been of tremendous importance for planetary science. The flagship space observatory Hubble Space Telescope has produced ground-breaking Solar System science, but when launched it did not even have the capability to track moving targets. The next astrophysics flagship mission, the James Webb Space Telescope, included Solar System scientists in its science team from the earliest days, with the result that Webb will launch with a diverse program and capabilities for Solar System exploration. The New Great Observatories, as well as future ground-based facilities, offer the opportunity for a robust suite of observations that will complement, enhance, and enable future Solar System exploration. We encourage the Planetary Science and Astrobiology Decadal Survey to overtly acknowledge the prospects for excellent Solar System science with the next generation of astrophysics facilities. We hope the Planetary Decadal will further encourage these missions to continue to formally involve Solar System scientists in the science working groups and development teams.