Hasil untuk "Labor in politics. Political activity of the working class"

Tom Bidewell, Artemis Deligianni, Tuğrulcan Elmas et al.

The influence of gender on online political communication remains contested, with existing scholarship providing mixed evidence as to whether gender shapes political messaging in digital environments. However, this debate has largely centred on mainstream platforms such as X (formerly Twitter), leaving the dynamics of alt-tech social media underexamined. This paper addresses this gap by analysing gendered patterns of political communication on Truth Social, a hyper-partisan platform that functions as a hub for the most committed followers of the American far right, a community closely associated with hegemonic masculine norms. To address this gap, we present the first large-scale analysis of political elite communication on Truth Social, using a novel dataset of 107k posts from 129 U.S. political figures. We examine the extent to which gender influences rhetorical style, topic framing, and audience engagement. We find that many gendered communication patterns documented on mainstream platforms persist on Truth Social. In particular, women political elites tend to express more joy and less anger than men and receive significantly higher levels of audience engagement. At the same time, more nuanced differences emerge. Although men and women political elites discuss largely similar conservative themes, they differ in how these issues are framed and in the rhetorical strategies employed. Notably, posts associated with women political elites contain higher levels of fear-based rhetoric, potentially suggesting selective adaptation in communicative style to navigate gender norms on the platform. These findings suggest that on Truth Social, an alt-tech platform with distinct ideological characteristics, mainstream gendered constraints persist, but are expressed through platform-specific communicative patterns shaped by its partisan orientation and sociotechnical environment.

Yueqing Liang, Liangwei Yang, Chen Wang et al.

Large Language Models (LLMs) have achieved significant advances in natural language processing, yet their potential for high-stake political decision-making remains largely unexplored. This paper addresses the gap by focusing on the application of LLMs to the United Nations (UN) decision-making process, where the stakes are particularly high and political decisions can have far-reaching consequences. We introduce a novel dataset comprising publicly available UN Security Council (UNSC) records from 1994 to 2024, including draft resolutions, voting records, and diplomatic speeches. Using this dataset, we propose the United Nations Benchmark (UNBench), the first comprehensive benchmark designed to evaluate LLMs across four interconnected political science tasks: co-penholder judgment, representative voting simulation, draft adoption prediction, and representative statement generation. These tasks span the three stages of the UN decision-making process--drafting, voting, and discussing--and aim to assess LLMs' ability to understand and simulate political dynamics. Our experimental analysis demonstrates the potential and challenges of applying LLMs in this domain, providing insights into their strengths and limitations in political science. This work contributes to the growing intersection of AI and political science, opening new avenues for research and practical applications in global governance. The UNBench Repository can be accessed at: https://github.com/yueqingliang1/UNBench.

Janneke Pieters, Ana Kujundzic, Rulof Burger et al.

Technological change can have profound impacts on the labor market. Decades of research have made it clear that technological change produces winners and losers. Machines can replace some types of work that humans do, while new technologies increase human's productivity in other types of work. For a long time, highly educated workers benefitted from increased demand for their labor due to skill-biased technological change, while the losers were concentrated at the bottom of the wage distribution (Katz and Autor, 1999; Goldin and Katz, 2007, 2010; Kijima, 2006). Currently, however, labor markets seem to be affected by a different type of technological change, the so-called routine-biased technological change (RBTC). This chapter studies the risk of automation in developing country labor markets, with a particular focus on differences between men and women. Given the pervasiveness of gender occupational segregation, there may be important gender differences in the risk of automation. Understanding these differences is important to ensure progress towards equitable development and gender inclusion in the face of new technological advances. Our objective is to describe the gender gap in the routine task intensity of jobs in developing countries and to explore the role of occupational segregation and several worker characteristics in accounting for the gender gap.

Arthur Buzelin, Pedro Robles Dutenhefner, Marcelo Sartori Locatelli et al.

Social media networks have amplified the reach of social and political movements, but most research focuses on mainstream platforms such as X, Reddit, and Facebook, overlooking Discord. As a rapidly growing, community-driven platform with optional decentralized moderation, Discord offers unique opportunities to study political discourse. This study analyzes over 30 million messages from political servers on Discord discussing the 2024 U.S. elections. Servers were classified as Republican-aligned, Democratic-aligned, or unaligned based on their descriptions. We tracked changes in political conversation during key campaign events and identified distinct political valence and implicit biases in semantic association through embedding analysis. We observed that Republican servers emphasized economic policies, while Democratic servers focused on equality-related and progressive causes. Furthermore, we detected an increase in toxic language, such as sexism, in Republican-aligned servers after Kamala Harris's nomination. These findings provide a first look at political behavior on Discord, highlighting its growing role in shaping and understanding online political engagement.

Leif Azzopardi, Yashar Moshfeghi

Political bias in Large Language Models (LLMs) presents a growing concern for the responsible deployment of AI systems. Traditional audits often attempt to locate a model's political position as a point estimate, masking the broader set of ideological boundaries that shape what a model is willing or unwilling to say. In this paper, we draw upon the concept of the Overton Window as a framework for mapping these boundaries: the range of political views that a given LLM will espouse, remain neutral on, or refuse to endorse. To uncover these windows, we applied an auditing-based methodology, called PRISM, that probes LLMs through task-driven prompts designed to elicit political stances indirectly. Using the Political Compass Test, we evaluated twenty-eight LLMs from eight providers to reveal their distinct Overton Windows. While many models default to economically left and socially liberal positions, we show that their willingness to express or reject certain positions varies considerably, where DeepSeek models tend to be very restrictive in what they will discuss and Gemini models tend to be most expansive. Our findings demonstrate that Overton Windows offer a richer, more nuanced view of political bias in LLMs and provide a new lens for auditing their normative boundaries.

Mats Faulborn, Indira Sen, Max Pellert et al.

Prompt-based language models like GPT4 and LLaMa have been used for a wide variety of use cases such as simulating agents, searching for information, or for content analysis. For all of these applications and others, political biases in these models can affect their performance. Several researchers have attempted to study political bias in language models using evaluation suites based on surveys, such as the Political Compass Test (PCT), often finding a particular leaning favored by these models. However, there is some variation in the exact prompting techniques, leading to diverging findings, and most research relies on constrained-answer settings to extract model responses. Moreover, the Political Compass Test is not a scientifically valid survey instrument. In this work, we contribute a political bias measured informed by political science theory, building on survey design principles to test a wide variety of input prompts, while taking into account prompt sensitivity. We then prompt 11 different open and commercial models, differentiating between instruction-tuned and non-instruction-tuned models, and automatically classify their political stances from 88,110 responses. Leveraging this dataset, we compute political bias profiles across different prompt variations and find that while PCT exaggerates bias in certain models like GPT3.5, measures of political bias are often unstable, but generally more left-leaning for instruction-tuned models. Code and data are available on: https://github.com/MaFa211/theory_grounded_pol_bias

Yejin Bang, Delong Chen, Nayeon Lee et al.

We propose to measure political bias in LLMs by analyzing both the content and style of their generated content regarding political issues. Existing benchmarks and measures focus on gender and racial biases. However, political bias exists in LLMs and can lead to polarization and other harms in downstream applications. In order to provide transparency to users, we advocate that there should be fine-grained and explainable measures of political biases generated by LLMs. Our proposed measure looks at different political issues such as reproductive rights and climate change, at both the content (the substance of the generation) and the style (the lexical polarity) of such bias. We measured the political bias in eleven open-sourced LLMs and showed that our proposed framework is easily scalable to other topics and is explainable.

Mercedes López Cantera

Reseña de Enzo Traverso, Revolución. Una historia intelectual, Buenos Aires, FCE, 2022, 615 pgs.

Laura Scoppetta

Resumen de Richard Evans. Eric Hobsbawm. Una vida en la historia, Buenos Aires, Crítica, 2021, 878 pgs.

Jukka Ruohonen

There is an increasing supply and demand for political advertising throughout the world. At the same time, societal threats, such as election interference by foreign governments and other bad actors, continues to be a pressing concern in many democracies. Furthermore, manipulation of electoral outcomes, whether by foreign or domestic forces, continues to be a concern of many citizens who are also worried about their fundamental rights. To these ends, the European Union (EU) has launched several initiatives for tackling the issues. A new regulation was proposed in 2020 also for improving the transparency of political advertising in the union. This short commentary reviews the regulation proposed and raises a few points about its limitations and potential impacts.

Sofía M del Pozo, Sebastián Pinto, Matteo Serafino et al.

In an era marked by an abundance of news sources, access to information significantly influences public opinion. Notably, the bias of news sources often serves as an indicator of individuals' political leanings. This study explores this hypothesis by examining the news sharing behavior of politically active social media users, whose political ideologies were identified in a previous study. Using correspondence analysis, we estimate the Media Sharing Index (MSI), a measure that captures bias in media outlets and user preferences within a hidden space. During Argentina's 2019 election on Twitter, we observed a predictable pattern: center-right individuals predominantly shared media from center-right biased outlets. However, it is noteworthy that those with center-left inclinations displayed a more diverse media consumption, which is a significant finding. Despite a noticeable polarization based on political affiliation observed in a retweet network analysis, center-left users showed more diverse media sharing preferences, particularly concerning the MSI. Although these findings are specific to Argentina, the developed methodology can be applied in other countries to assess the correlation between users' political leanings and the media they share.

Orestis Papakyriakopoulos, Severin Engelmann, Amy Winecoff

A significant share of political discourse occurs online on social media platforms. Policymakers and researchers try to understand the role of social media design in shaping the quality of political discourse around the globe. In the past decades, scholarship on political discourse theory has produced distinct characteristics of different types of prominent political rhetoric such as deliberative, civic, or demagogic discourse. This study investigates the relationship between social media reaction mechanisms (i.e., upvotes, downvotes) and political rhetoric in user discussions by engaging in an in-depth conceptual analysis of political discourse theory. First, we analyze 155 million user comments in 55 political subforums on Reddit between 2010 and 2018 to explore whether users' style of political discussion aligns with the essential components of deliberative, civic, and demagogic discourse. Second, we perform a quantitative study that combines confirmatory factor analysis with difference in differences models to explore whether different reaction mechanism schemes (e.g., upvotes only, upvotes and downvotes, no reaction mechanisms) correspond with political user discussion that is more or less characteristic of deliberative, civic, or demagogic discourse. We produce three main takeaways. First, despite being "ideal constructs of political rhetoric," we find that political discourse theories describe political discussions on Reddit to a large extent. Second, we find that discussions in subforums with only upvotes, or both up- and downvotes are associated with user discourse that is more deliberate and civic. Third, social media discussions are most demagogic in subreddits with no reaction mechanisms at all. These findings offer valuable contributions for ongoing policy discussions on the relationship between social media interface design and respectful political discussion among users.

Yuwei Chuai, Jichang Zhao, Nicolas Pröllochs et al.

Political elites play an important role in the proliferation of online misinformation. However, an understanding of how fact-checking platforms pick up politicized misinformation for fact-checking is still in its infancy. Here, we conduct an empirical analysis of mentions of U.S. political elites within fact-checked statements. For this purpose, we collect a comprehensive dataset consisting of 35,014 true and false statements that have been fact-checked by two major fact-checking organizations (Snopes, PolitiFact) in the U.S. between 2008 and 2023, i.e., within an observation period of 15 years. Subsequently, we perform content analysis and explanatory regression modeling to analyze how veracity is linked to mentions of U.S. political elites in fact-checked statements. Our analysis yields the following main findings: (i) Fact-checked false statements are, on average, 20% more likely to mention political elites than true fact-checked statements. (ii) There is a partisan asymmetry such that fact-checked false statements are 88.1% more likely to mention Democrats, but 26.5% less likely to mention Republicans, compared to fact-checked true statements. (iii) Mentions of political elites in fact-checked false statements reach the highest level during the months preceding elections. (iv) Fact-checked false statements that mention political elites carry stronger other-condemning emotions and are more likely to be pro-Republican, compared to fact-checked true statements. In sum, our study offers new insights into understanding mentions of political elites in false statements on U.S. fact-checking platforms, and bridges important findings at the intersection between misinformation and politicization.

Beomjune Kim, Eunsun Lee, Dongbin Na

Many users reading online articles in various magazines may suffer considerable difficulty in distinguishing the implicit intents in texts. In this work, we focus on automatically recognizing the political intents of a given online newspaper by understanding the context of the text. To solve this task, we present a novel Korean text classification dataset that contains various articles. We also provide deep-learning-based text classification baseline models trained on the proposed dataset. Our dataset contains 12,000 news articles that may contain political intentions, from the politics section of six of the most representative newspaper organizations in South Korea. All the text samples are labeled simultaneously in two aspects (1) the level of political orientation and (2) the level of pro-government. To the best of our knowledge, our paper is the most large-scale Korean news dataset that contains long text and addresses multi-task classification problems. We also train recent state-of-the-art (SOTA) language models that are based on transformer architectures and demonstrate that the trained models show decent text classification performance. All the codes, datasets, and trained models are available at https://github.com/Kdavid2355/KoPolitic-Benchmark-Dataset.

Samyla de Almeida Silva, Luísa Pessoni de Carvalho Garcia, Thiago Henrique Evangelista Alves et al.

O artigo tem o objetivo de despertar reflexões em defesa do uso do termo “violência obstétrica” frente à postura do Ministério da Saúde e do Conselho Federal de Medicina em evitá-lo e aboli-lo. Entende-se que essa postura anula a constituição social, histórica e cultural da expressão e banaliza a agressão sofrida por mulheres no momento, durante e após o parto. Dessa forma, buscou-se demonstrar a importância do uso do vocábulo por meio de conceituações e caracterizações, bem como pela abordagem de breve histórico sobre a sua gradual notoriedade como questão de violação dos direitos humanos fundamentais. Ademais, o trabalho sugere a manutenção da expressão e espera colaborar para a sensibilização do leitor sobre esse tipo de violência.

Alysson Rodrigo Fonseca e Silva, Gabriella Ribeiro Coelho Melo, Mariana Caetano et al.

O objetivo do projeto “Horta na escola” foi promover a Educação Ambiental, bem como despertar valores socioambientais, utilizando a horta como estratégia didático-pedagógica, na Escola Estadual Armando Nogueira, em Divinópolis-MG. Participaram das atividades duas turmas do 6º ano (n = 43 alunos), duas turmas do 7º ano (n = 45), duas turmas do 8º (n = 46) e duas turmas do 9º ano (n = 49), correspondendo a um total de 183 estudantes, e quatro professores, além de dois funcionários da escola que auxiliaram na confecção da horta. Além da horta, foi proposta a criação de uma composteira, visando o aproveitamento de resíduos orgânicos provenientes da cozinha escolar. As ações foram conduzidas de forma que os estudantes pudessem participar na preparação dos canteiros, semeadura das hortaliças, confecção e uso da composteira. As atividades permitiram trabalhar diversos conteúdos, como reciclagem de matéria orgânica, nutrição humana, solo e interações ecossistêmicas. Foram ministradas também duas palestras, uma sobre reciclagem e a outra sobre a importância de uma boa alimentação, cada uma envolvendo um montante de 172 e 183 estudantes respectivamente. O projeto de extensão mostrou-se uma importante ferramenta de Educação Ambiental, aumentando o interesse, aprendizado e a conscientização do público envolvido.

Laura Ruocco

Reseña de Mujeres que trabajan. Labores femeninas, Estado y sindicatos (Buenos Aires,1910-1960), Buenos Aires, Grupo Editor Universitario-EUDEM, 2020, 178 pp.

Erich Wolff, Felipe Munoz

This article interrogates the techno-politics of crowdsourced data in the study of environmental hazards such as floods, storms, wildfires, and cyclones. We highlight some of the main debates around the use of citizen-generated data for assessing, monitoring, and responding to disasters. We then argue that, compared to the number of articles discussing the quality of citizen-generated data, little attention has been dedicated to discussing the social and political implications of this kind of practice. While this article does not intend to present definitive answers, it outlines inevitable challenges and indicates potential directions for future studies on the techno-politics of disaster data collection. Within a techno-politics approach, we argue for a model of political participation that recognizes citizens providing data to shape cities as equal experts in the production of knowledge and decision-making, rather than external contributors collecting data for formal authorities. This political participation approach, we believe, would increase the dependence of formal scientific knowledge on citizens' daily-lived experiences, create horizontal collaborations among diverse stakeholders, in terms of respect and recognition, and increase the humanization of marginalized communities, particularly from the Global South.

Joey Huang

In this paper, some main eigenvalues and eigenvectors of the politics matrix are investigated. The number of upper-class families in a society is the number of eigenvalues which are very close to 1. An algorithm to identify all the upper-class families from the right and left eigenvectors of those eigenvalues is developed.

Boluwatife Ajibola

Social media provides an extended space for collective action, as netizens leverage it as a tool for claim-making and for demanding the dividends of governance. However, political regimes often greet expanding use of social media with censorship, which netizens often have to contend with, particularly in the quest for development outcomes. While existing studies having expansively explored multiple uses of social media, the specific features that signal their massive uptake and how this intersects with the quest for political power has not been substantially documented. This paper argues that social media is characterized by social buttons that expedite the multiplication of 'digital bullets' - in forms of tweets and perceived detestable comments - which compromise the defense lines of political regimes, hence, their uptake of censorship as metaphorical 'flak jackets'. This research is conducted on the basis of key informant interviews with voices against social media censorship in Nigeria since the inception of Nigeria's ruling government in 2015, particularly following the proposed 'Protection from Internet Falsehood and Manipulations Bill' in 2019.