Hasil untuk "Political Science"

Anup Agrawal, George P. Baker, Sudip Bhattacharya et al.

Amanda Dória de Assis, Bruna Teixeira Santos

Este trabalho tematiza a Educação para as Relações Étnico-Raciais (ERER). Trata-se de um estudo qualitativo, fundamentado na lente transmetodológica. Sob esta perspectiva, a partir de uma revisão bibliográfica, somada às nossas experiências e observações cotidianas como professoras e pesquisadoras, problematizamos os efeitos da supremacia branca na educação brasileira, destacando os desafios ainda presentes na implementação efetiva da Educação para as Relações Étnico-Raciais. Com este estudo, compreendemos que a branquitude, como fenômeno social, não apenas opera na estrutura curricular, mas permeia relações, práticas pedagógicas e concepções institucionais, contribuindo para a reiteração de hierarquias no espaço escolar. Ao mesmo tempo, ressaltamos as resistências e os movimentos que têm surgido na contramão dessas lógicas colonialistas e supremacistas, como as modificações efetivadas em leis e diretrizes, as de pedagogias decoloniais.

Jorge Martinez-Palomera, Amy Tuson, TESS Science Support Center

3I/ATLAS is the third known interstellar object to pass through our Solar System. NASA's Transiting Exoplanet Survey Satellite (TESS) made dedicated observations of 3I/ATLAS between 15 -- 22 January 2026 (Sector 1751), capturing high-cadence observations at 200s and 20s cadence. We present two High Level Science Products (HLSPs): (1) comet-centered image time series, corrected for background scattered light and stars; and (2) aperture light curves extracted from the corrected images. We created these data products using the official TESS products and they are publicly available at the Mikulski Archive for Space Telescopes (MAST). TESS's high-precision, near-continuous photometry will provide unique insights into the comet's activity following its closest approach to the Sun. The TESS Science Support Center (TSSC) has created these data products to facilitate scientific analyses by the TESS and Solar System communities.

Dipto Das, Afrin Prio, Pritu Saha et al.

This paper examines how non-resident Bangladeshis mobilized during the 2024 quota-reform turned pro-democracy movement, leveraging social platforms and remittance flows to challenge state authority. Drawing on semi-structured interviews, we identify four phases of their collective action: technology-mediated shifts to active engagement, rapid transnational network building, strategic execution of remittance boycott, reframing economic dependence as political leverage, and adaptive responses to government surveillance and information blackouts. We extend postcolonial computing by introducing the idea of "diasporic superposition," which shows how diasporas can exercise political and economic influence from hybrid positionalities that both contest and complicate power asymmetries. We reframe diaspora engagement by highlighting how migrants participate in and reshape homeland politics, beyond narratives of integration in host countries. We advance the scholarship on financial technologies by foregrounding their relationship with moral economies of care, state surveillance, regulatory constraints, and uneven international economic power dynamics. Together, these contributions theorize how transnational activism and digital technologies intersect to mobilize political change in Global South contexts.

Adir Elmakais, Oren Glickman

Israeli society has experienced significant political polarization in recent years, reflected in five Knesset elections held within a four-year period (2019-2022). Public discourse increasingly references hypothetical divisions of the country into politically homogeneous "cantons." This paper develops a data-driven algorithmic approach to explore such divisions using publicly available municipality-level election results and geographic boundary data from the Israel Central Bureau of Statistics. We partition 229 Israeli municipalities into geographically contiguous cantons that maximize internal political similarity. Our methodology employs four clustering algorithms -- Simulated Annealing, Agglomerative Clustering with contiguity constraints, Louvain Community Detection, and K-Means (baseline) -- evaluated across four feature representations (BlocShares, RawParty, PCA, NMF), three distance metrics (Euclidean, Cosine, Jensen-Shannon), and six values of K (3-20), yielding 264 experimental configurations. Key results show that BlocShares with Euclidean distance and Agglomerative clustering produces the highest clustering quality (silhouette score 0.905), while NMF with Louvain community detection achieves the best balance between political homogeneity, silhouette quality (0.121), and interpretable canton assignments. Temporal stability analysis across all five elections reveals that deterministic algorithms produce near-perfectly stable partitions (ARI up to 1.0), while Israel's political geography remains structurally consistent despite electoral volatility. The resulting K=5 partition identifies five politically coherent regions -- a center-leaning metropolitan core, a right-wing southern arc, a right-leaning northern mixed region, and two Arab-majority cantons -- closely reflecting known political-demographic divisions. An interactive web application accompanies this work.

Salah Feras Alali, Mohammad Nashat Maasfeh, Mucahid Kutlu et al.

With the incredible advancements in Large Language Models (LLMs), many people have started using them to satisfy their information needs. However, utilizing LLMs might be problematic for political issues where disagreement is common and model outputs may reflect training-data biases or deliberate alignment choices. To better characterize such behavior, we assess the stances of nine LLMs on 24 politically sensitive issues using five prompting techniques. We find that models often adopt opposing stances on several issues; some positions are malleable under prompting, while others remain stable. Among the models examined, Grok-3-mini is the most persistent, whereas Mistral-7B is the least. For issues involving countries with different languages, models tend to support the side whose language is used in the prompt. Notably, no prompting technique alters model stances on the Qatar blockade or the oppression of Palestinians. We hope these findings raise user awareness when seeking political guidance from LLMs and encourage developers to address these concerns.

Débora Pinto, Paula C. N. Figueiredo, Nuno J. P. Rodrigues

This study examines the relationship between e-leadership competencies—assessed through a E-Leadership Competencies (SEC) model—and organizational preference for telework in Portugal. In the context of increasing digitalization and following the widespread experience of remote work driven by the COVID-19 pandemic, it becomes essential to understand the role of e-competence in leading geographically dispersed teams. A quantitative investigation was conducted through the application of an online questionnaire to e-leaders of companies based in Portugal whose teams benefit from telework arrangements. The results indicate that only three of the six e-competencies identified in the SEC model show statistical significance in e-leadership effectiveness, with no relationship observed between perceived effectiveness and organizational investment in telework. Nevertheless, more than 80% of respondents reported that telework has been increasing within their organizations. This study contributes to the adaptation of the SEC model to the Portuguese context and reinforces its importance as a tool for diagnosing and developing e-leadership competencies. Theoretical and practical implications highlight the need to explore new dimensions—including hard skills—and applying the model across different sectors and types of organizations, thus supporting the preparation of e-leaders for an increasingly digital world of work. Overall, by evidencing the SEC model’s successful adaptation in Portugal, the findings underscore the model’s broader applicability and potential for generalization across diverse organizational settings.

Barthélémy Toumgbin DELLA , Fernand Kouassi GNAHOUE

L’objectif de cette étude est d’amener les usagers de la machine intelligente à réduire au mieux ses impacts nocifs sur l’intersubjectivité. À partir des démarches descriptive, critique et prospectives, nos résultats sont ainsi présentésː l’intelligence artificielle est la principale marque du machinisme contemporain. Par conséquent, la machine intelligente tend à se substituer à l’autre dans l’intersubjectivité. Pour éviter cette situation, il s’avère nécessaire d’envisager un machinisme sur fond d’humanisme. En somme, l’impact de la machine intelligente sur l’intersubjectivité est fonction de la posture que se donne notre liberté. Intelligence artificielle, Intersubjectivité, Liberté, Machinisme contemporain, Oubli de l’autre

Vera Sosnovik, Caroline Violot, Mathias Humbert

YouTube has emerged as a major platform for political communication and news dissemination, particularly during high-stakes electoral periods. In the context of the 2024 European Parliament and French legislative elections, this study investigates how political actors and news media used YouTube to shape public discourse. We analyze over 100,000 video transcripts and metadata from 74 French YouTube channels operated by national news outlets, local media, and political figures. To identify the key themes emphasized during the campaign period, we applied a semi-automated method that combined large language models with clustering and manual review. The results reveal distinct thematic patterns across the political spectrum and media types, with right-leaning news outlets focusing on topics like immigration, while left-leaning emphasized protest and media freedom. Themes generating the most audience engagement, measured by comment-to-view ratios, were most often the most polarizing ones. In contrast, less polarizing themes such as video games and nature showed higher approval, reflected in like-to-view ratios. We also observed a general tendency across all media types to portray political figures in neutral or critical terms rather than favorable ones.

Akram Elbouanani, Evan Dufraisse, Adrian Popescu

Political biases encoded by LLMs might have detrimental effects on downstream applications. Existing bias analysis methods rely on small-size intermediate tasks (questionnaire answering or political content generation) and rely on the LLMs themselves for analysis, thus propagating bias. We propose a new approach leveraging the observation that LLM sentiment predictions vary with the target entity in the same sentence. We define an entropy-based inconsistency metric to encode this prediction variability. We insert 1319 demographically and politically diverse politician names in 450 political sentences and predict target-oriented sentiment using seven models in six widely spoken languages. We observe inconsistencies in all tested combinations and aggregate them in a statistically robust analysis at different granularity levels. We observe positive and negative bias toward left and far-right politicians and positive correlations between politicians with similar alignment. Bias intensity is higher for Western languages than for others. Larger models exhibit stronger and more consistent biases and reduce discrepancies between similar languages. We partially mitigate LLM unreliability in target-oriented sentiment classification (TSC) by replacing politician names with fictional but plausible counterparts.

Leonardo Becchetti, Nazaria Solferino

We explore the political and ideological positioning of ChatGPT, a leading large language model (LLM), by comparing its responses to political economy questions from the European Social Survey (ESS). The questions concern environmental sustainability, civil rights, income inequality, and government size. ChatGPT's self-assessed placement on a left-right political spectrum is compared to the ideological stances of individuals providing similar answers in the ESS dataset. Results highlight a significant left-oriented bias in ChatGPT's answers, particularly on environmental and civil rights topics, diverging from its same self-declared center-left stance. These findings underscore the need for transparency in AI systems to prevent potential ideological influences on users. We conclude by discussing the implications for AI governance, debiasing strategies, and educational use.

Sooahn Meier, Kerstin Martens

The COVID-19 pandemic has triggered turbulent times across the globe, reminding us of the highly multidimensional and interdependent nature of today's world. Next to diverging national attempts to constrain the spread of the virus, numerous international organizations worked intensely to minimize the impacts of the disease on a regional or/and global scale. Albeit not considered a conventional agency responsible for global infectious diseases, the Organization for Economic Co-operation and Development (OECD) has surprisingly been one of the most proactive IOs in the pandemic response. In this context, this article examines to what extent the OECD's COVID-19 pandemic response adheres to the role of a global crisis manager. By adapting the theoretical concepts of crisis leadership, we explore the extent of sense-making, decision-making, and learning capacities of the OECD during the pandemic, upon which we draw the organization's position-making. Based on expert interviews and document analysis, this article illustrates that the OECD's concerns regarding the pandemic's severe effects across socioeconomic sectors focused exclusively on its member states. This sense-making enabled prompt and multilayered top-down as well as bottom-up decision-making to provide member states with policy options as solutions to the new challenges. However, the OECD's engagement during the crisis was proactive only to the extent that several limitations allowed, such as resource inflexibility and internal dynamics between the Secretariat and member states. In conclusion, we argue that the OECD did not present itself to be a global crisis manager during the COVID-19 pandemic. Rather, the IO's responses consolidated its position-making as a policy advisor for member states.

Чижо Э., Богданова Н., Миетуле И. et al.

Несмотря на широкое распространение цифровых технологий и их потенциал для снижения традиционных барьеров в бизнесе и коммуникации, существует значительное неравенство в доступе к инструментам цифрового маркетинга и выгодам от их использования среди жителей и предприятий Латвии. Целью данной статьи является анализ неравенства среди жителей и предприятий на латвийском интернет-рынке цифрового маркетинга. Концептуальную основу исследования составляют модель принятия технологии, теория цифрового разрыва и основанный на теории социальных полей ресурсный подход в стратификационных исследованиях. Для динамического анализа статистических данных используется метод оценки кон(ди)вергенции показателей включенности различных социально-демографических и географических групп жителей и предприятий Латвии в интернет-рынок цифрового маркетинга. Эмпирической основой данного исследования являются данные латвийской статистики за 2013—2022 гг. (по некоторым показателям — 2023 г.). Результаты исследования показывают, что развитие цифрового маркетинга в Латвии происходит очень быстро, но при этом потенциал для развития все еще остается очень большим, поскольку при 90 %-ном удельном весе жителей Латвии, регулярно (хотя бы раз в неделю) использующих интернет, более 30 % латвийцев пока что ни разу не сделали покупку или заказ в интернете. Развитие цифрового маркетинга в Латвии снижает социально-демографическое и географическое неравенство среди жителей и предприятий на цифровом рынке по отношению к «цифровому неравенству входа» (доступа к интернет-рынку), но по отношению к «цифровому неравенству выхода» (отдачи от этого доступа) выравнивающие возможности цифрового маркетинга в Латвии (особенно в ее регионах) ограничены спецификой функционирования экономики, основанной на социальном капитале, в которой практически не работают модели и теории, разработанные для экономики инноваций. Новизну данного исследования составляет комплексный анализ общего фона и динамики развития латвийского интернет-рынка цифрового маркетинга в контексте цифрового неравенства среди жителей и предприятий.

Evan Dufraisse, Adrian Popescu, Julien Tourille et al.

Researchers and practitioners interested in computational politics rely on automatic content analysis tools to make sense of the large amount of political texts available on the Web. Such tools should provide objective and subjective aspects at different granularity levels to make the analyses useful in practice. Existing methods produce interesting insights for objective aspects, but are limited for subjective ones, are often limited to national contexts, and have limited explainability. We introduce a text analysis framework which integrates both perspectives and provides a fine-grained processing of subjective aspects. Information retrieval techniques and knowledge bases complement powerful natural language processing components to allow a flexible aggregation of results at different granularity levels. Importantly, the proposed bottom-up approach facilitates the explainability of the obtained results. We illustrate its functioning with insights on news outlets, political orientations, topics, individual entities, and demographic segments. The approach is instantiated on a large corpus of French news, but is designed to work seamlessly for other languages and countries.

Ahana Biswas, Yu-Ru Lin, Yuehong Cassandra Tai et al.

Elected officials have privileged roles in public communication. In contrast to national politicians, whose posting content is more likely to be closely scrutinized by a robust ecosystem of nationally focused media outlets, sub-national politicians are more likely to openly disseminate harmful content with limited media scrutiny. In this paper, we analyze the factors that explain the online visibility of over 6.5K unique state legislators in the US and how their visibility might be impacted by posting low-credibility or uncivil content. We conducted a study of posting on Twitter and Facebook (FB) during 2020-21 to analyze how legislators engage with users on these platforms. The results indicate that distributing content with low-credibility information attracts greater attention from users on FB and Twitter for Republicans. Conversely, posting content that is considered uncivil on Twitter receives less attention. A noticeable scarcity of posts containing uncivil content was observed on FB, which may be attributed to the different communication patterns of legislators on these platforms. In most cases, the effect is more pronounced among the most ideologically extreme legislators. Our research explores the influence exerted by state legislators on online political conversations, with Twitter and FB serving as case studies. Furthermore, it sheds light on the differences in the conduct of political actors on these platforms. This study contributes to a better understanding of the role that political figures play in shaping online political discourse.

Luca Foppiano, Guillaume Lambard, Toshiyuki Amagasa et al.

This study is dedicated to assessing the capabilities of large language models (LLMs) such as GPT-3.5-Turbo, GPT-4, and GPT-4-Turbo in extracting structured information from scientific documents in materials science. To this end, we primarily focus on two critical tasks of information extraction: (i) a named entity recognition (NER) of studied materials and physical properties and (ii) a relation extraction (RE) between these entities. Due to the evident lack of datasets within Materials Informatics (MI), we evaluated using SuperMat, based on superconductor research, and MeasEval, a generic measurement evaluation corpus. The performance of LLMs in executing these tasks is benchmarked against traditional models based on the BERT architecture and rule-based approaches (baseline). We introduce a novel methodology for the comparative analysis of intricate material expressions, emphasising the standardisation of chemical formulas to tackle the complexities inherent in materials science information assessment. For NER, LLMs fail to outperform the baseline with zero-shot prompting and exhibit only limited improvement with few-shot prompting. However, a GPT-3.5-Turbo fine-tuned with the appropriate strategy for RE outperforms all models, including the baseline. Without any fine-tuning, GPT-4 and GPT-4-Turbo display remarkable reasoning and relationship extraction capabilities after being provided with merely a couple of examples, surpassing the baseline. Overall, the results suggest that although LLMs demonstrate relevant reasoning skills in connecting concepts, specialised models are currently a better choice for tasks requiring extracting complex domain-specific entities like materials. These insights provide initial guidance applicable to other materials science sub-domains in future work.

Tinatin Osmonova, Alexey Tikhonov, Ivan P. Yamshchikov

With the rise of computational social science, many scholars utilize data analysis and natural language processing tools to analyze social media, news articles, and other accessible data sources for examining political and social discourse. Particularly, the study of the emergence of echo-chambers due to the dissemination of specific information has become a topic of interest in mixed methods research areas. In this paper, we analyze data collected from two news portals, Breitbart News (BN) and New York Times (NYT) to prove the hypothesis that the formation of echo-chambers can be partially explained on the level of an individual information consumption rather than a collective topology of individuals' social networks. Our research findings are presented through knowledge graphs, utilizing a dataset spanning 11.5 years gathered from BN and NYT media portals. We demonstrate that the application of knowledge representation techniques to the aforementioned news streams highlights, contrary to common assumptions, shows relative "internal" neutrality of both sources and polarizing attitude towards a small fraction of entities. Additionally, we argue that such characteristics in information sources lead to fundamental disparities in audience worldviews, potentially acting as a catalyst for the formation of echo-chambers.

Dieter Grimm

The German Constitution (»Basic Law«) of 1949 is generally regarded as a successful and effective constitution. Many attribute the altogether lucky development of the Federal Republic of Germany not least to this constitution and its interpretation and implementation by the Federal Constitutional Court. However, neither the Basic Law nor the jurisprudence of the Constitutional Court play a significant role in the books of historians on the Federal Republic. The article argues that the influence of constitutional law on political behavior and social relations is a decisive factor for the situations, developments and events that historians want to describe and explain. A number of examples show where the objects of contemporary historiography cannot be adequately understood and interpreted without regard to the Basic Law and the judgements of the Federal Constitutional Court.

Mathias-Felipe de-Lima-Santos, Isabella Gonçalves, Marcos G. Quiles et al.

In today's digital age, images have emerged as powerful tools for politicians to engage with their voters on social media platforms. Visual content possesses a unique emotional appeal that often leads to increased user engagement. However, research on visual communication remains relatively limited, particularly in the Global South. This study aims to bridge this gap by employing a combination of computational methods and qualitative approach to investigate the visual communication strategies employed in a dataset of 11,263 Instagram posts by 19 Brazilian presidential candidates in 2018 and 2022 national elections. Through two studies, we observed consistent patterns across these candidates on their use of visual political communication. Notably, we identify a prevalence of celebratory and positively toned images. They also exhibit a strong sense of personalization, portraying candidates connected with their voters on a more emotional level. Our research also uncovers unique contextual nuances specific to the Brazilian political landscape. We note a substantial presence of screenshots from news websites and other social media platforms. Furthermore, text-edited images with portrayals emerge as a prominent feature. In light of these results, we engage in a discussion regarding the implications for the broader field of visual political communication. This article serves as a testament to the pivotal role that Instagram has played in shaping the narrative of two fiercely polarized Brazilian elections, casting a revealing light on the ever-evolving dynamics of visual political communication in the digital age. Finally, we propose avenues for future research in the realm of visual political communication.

E. V. Molodyakova