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

Adib Sakhawat, Tahsin Islam, Takia Farhin et al.

As large language models (LLMs) are increasingly deployed, understanding how they express political positioning is important for evaluating alignment and downstream effects. We audit 26 contemporary LLMs using three political psychometric inventories (Political Compass, SapplyValues, 8Values) and a news bias labeling task. To test robustness, inventories are administered across multiple semantic prompt variants and analyzed with a two-way ANOVA separating model and prompt effects. Most models cluster in a similar ideological region, with 96.3% located in the Libertarian-Left quadrant of the Political Compass, and model identity explaining most variance across prompt variants ($η^2 > 0.90$). Cross-instrument comparisons suggest that the Political Compass social axis aligns more strongly with cultural progressivism than authority-related measures ($r=-0.64$). We observe differences between open-weight and closed-source models and asymmetric performance in detecting extreme political bias in downstream classification. Regression analysis finds that psychometric ideological positioning does not significantly predict classification errors, providing no evidence of a statistically significant relationship between conversational ideological identity and task-level behavior. These findings suggest that single-axis evaluations are insufficient and that multidimensional auditing frameworks are important to characterize alignment behavior in deployed LLMs. Our code and data are publicly available at https://github.com/sakhadib/PolAlignLLM.

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.

Maite Iglesias

Este trabajo revisa la división de la Federación Anarquista Uruguaya (FAU) de 1963-1964 a la luz de nuevos acervos documentales y realiza un primer esbozo de la historia de la Alianza Libertaria del Uruguay (ALU), organización que formaron algunas de las agrupaciones y personas de la FAU tras su escisión. La hipótesis es que la ALU pretendió preservar el tercerismo y el anticomunismo de izquierdas prósperos a fines de los años 50 en el movimiento anarquista uruguayo, en un contexto de realineamientos de las izquierdas ante la definición de la Revolución Cubana como marxista-leninista.  

Daniele Cirulli, Giulio Cimini, Giovanni Palermo

Large Language Models (LLMs) have recently emerged as powerful tools for natural language generation, with applications spanning from content creation to social simulations. Their ability to mimic human interactions raises both opportunities and concerns, particularly in the context of politically relevant online discussions. In this study, we evaluate the performance of LLMs in replicating user-generated content within a real-world, divisive scenario: Reddit conversations during the 2016 US Presidential election. In particular, we conduct three different experiments, asking GPT-4 to generate comments by impersonating either real or artificial partisan users. We analyze the generated comments in terms of political alignment, sentiment, and linguistic features, comparing them against real user contributions and benchmarking against a null model. We find that GPT-4 is able to produce realistic comments, both in favor of or against the candidate supported by the community, yet tending to create consensus more easily than dissent. In addition we show that real and artificial comments are well separated in a semantically embedded space, although they are indistinguishable by manual inspection. Our findings provide insights on the potential use of LLMs to sneak into online discussions, influence political debate and shape political narratives, bearing broader implications of AI-driven discourse manipulation.

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.

Pavlos Sermpezis, Stelios Karamanidis, Eva Paraschou et al.

Political discourse datasets are important for gaining political insights, analyzing communication strategies or social science phenomena. Although numerous political discourse corpora exist, comprehensive, high-quality, annotated datasets are scarce. This is largely due to the substantial manual effort, multidisciplinarity, and expertise required for the nuanced annotation of rhetorical strategies and ideological contexts. In this paper, we present AgoraSpeech, a meticulously curated, high-quality dataset of 171 political speeches from six parties during the Greek national elections in 2023. The dataset includes annotations (per paragraph) for six natural language processing (NLP) tasks: text classification, topic identification, sentiment analysis, named entity recognition, polarization and populism detection. A two-step annotation was employed, starting with ChatGPT-generated annotations and followed by exhaustive human-in-the-loop validation. The dataset was initially used in a case study to provide insights during the pre-election period. However, it has general applicability by serving as a rich source of information for political and social scientists, journalists, or data scientists, while it can be used for benchmarking and fine-tuning NLP and large language models (LLMs).

Douglas Markant, Subham Sah, Alireza Karduni et al.

Political sectarianism is fueled in part by misperceptions of political opponents: People commonly overestimate the support for extreme policies among members of the other party. Research suggests that correcting partisan misperceptions by informing people about the actual views of outparty members may reduce one's own expressed support for political extremism, including partisan violence and anti-democratic actions. The present study investigated how correction effects depend on different representations of outparty views communicated through data visualizations. We conducted an experiment with U.S. based participants from Prolific (N=239 Democrats, N=244 Republicans). Participants made predictions about support for political violence and undemocratic practices among members of their political outparty. They were then presented with data from an earlier survey on the actual views of outparty members. Some participants viewed only the average response (Mean-Only condition), while other groups were shown visual representations of the range of views from 75% of the outparty (Mean+Interval condition) or the full distribution of responses (Mean+Points condition). Compared to a control group that was not informed about outparty views, we observed the strongest correction effects among participants in the Mean-only and Mean+Points condition, while correction effects were weaker in the Mean+Interval condition. In addition, participants who observed the full distribution of out-party views (Mean+Points condition) were most accurate at later recalling the degree of support among the outparty. Our findings suggest that data visualizations can be an important tool for correcting pervasive distortions in beliefs about other groups. However, the way in which variability in outparty views is visualized can significantly shape how people interpret and respond to corrective information.

Christopher M. Wegemer, Edward Halim, Jeff Burke

Political polarization undermines democratic civic education by exacerbating identity-based resistance to opposing viewpoints. Emerging AI technologies offer new opportunities to advance interventions that reduce polarization and promote political open-mindedness. We examined novel design strategies that leverage adaptive and emotionally-responsive civic narratives that may sustain students' emotional engagement in stories, and in turn, promote perspective-taking toward members of political out-groups. Drawing on theories from political psychology and narratology, we investigate how affective computing techniques can support three storytelling mechanisms: transportation into a story world, identification with characters, and interaction with the storyteller. Using a design-based research (DBR) approach, we iteratively developed and refined an AI-mediated Digital Civic Storytelling (AI-DCS) platform. Our prototype integrates facial emotion recognition and attention tracking to assess users' affective and attentional states in real time. Narrative content is organized around pre-structured story outlines, with beat-by-beat language adaptation implemented via GPT-4, personalizing linguistic tone to sustain students' emotional engagement in stories that center political perspectives different from their own. Our work offers a foundation for AI-supported, emotionally-sensitive strategies that address affective polarization while preserving learner autonomy. We conclude with implications for civic education interventions, algorithmic literacy, and HCI challenges associated with AI dialogue management and affect-adaptive learning environments.

Clara Lachenmaier, Judith Sieker, Sina Zarrieß

Communication among humans relies on conversational grounding, allowing interlocutors to reach mutual understanding even when they do not have perfect knowledge and must resolve discrepancies in each other's beliefs. This paper investigates how large language models (LLMs) manage common ground in cases where they (don't) possess knowledge, focusing on facts in the political domain where the risk of misinformation and grounding failure is high. We examine the ability of LLMs to answer direct knowledge questions and loaded questions that presuppose misinformation. We evaluate whether loaded questions lead LLMs to engage in active grounding and correct false user beliefs, in connection to their level of knowledge and their political bias. Our findings highlight significant challenges in LLMs' ability to engage in grounding and reject false user beliefs, raising concerns about their role in mitigating misinformation in political discourse.

Ernesto Alejandro Najmias

Con una larga trayectoria en Argentina, las experiencias autogestivas enfrentaron durante la pandemia de COVID-19 (2020-2021) dificultades específicas, que pusieron en riesgo su continuidad. Este artículo indagará en la experiencia de la gráfica recuperada Madygraf (ex Donnelley), buscando recuperar las formas en que el nuevo contexto condicionó las estrategias desenvueltas por el colectivo obrero, que debió reconvertir su producción hacia la manufactura de sanitizante, establecer nuevas redes de solidaridad y encarar un proceso inédito de incorporación de maquinaria para la apertura de una nueva línea de producción ambientalmente sustentable. Se buscará reconstruir estas nuevas dinámicas de innovación social y adecuación socio-técnica, sus limitaciones, y las formas en que afectaron a la movilización y pliego de reclamos desplegados por los trabajadores.

Ho-Chun Herbert Chang, Benjamin Shaman, Yung-chun Chen et al.

Visual content on social media has become increasingly influential in shaping political discourse and civic engagement. Using a dataset of 239,526 Instagram images, deep learning, and LLM-based workflows, we examine the impact of different content types on user engagement during the 2024 US presidential Elections, with a focus on synthetic visuals. Results show while synthetic content may not increase engagement alone, it mediates how political information is created through highly effective, often absurd, political memes. We define the notion of generative memesis, where memes are no longer shared person-to-person but mediated by AI through customized, generated images. We also find partisan divergences: Democrats use AI for in-group support whereas Republicans use it for out-group attacks. Non-traditional, left-leaning outlets are the primary creators of political memes; emphasis on different topics largely follows issue ownership.

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.

Jillian Fisher, Shangbin Feng, Robert Aron et al.

As modern large language models (LLMs) become integral to everyday tasks, concerns about their inherent biases and their potential impact on human decision-making have emerged. While bias in models are well-documented, less is known about how these biases influence human decisions. This paper presents two interactive experiments investigating the effects of partisan bias in LLMs on political opinions and decision-making. Participants interacted freely with either a biased liberal, biased conservative, or unbiased control model while completing these tasks. We found that participants exposed to partisan biased models were significantly more likely to adopt opinions and make decisions which matched the LLM's bias. Even more surprising, this influence was seen when the model bias and personal political partisanship of the participant were opposite. However, we also discovered that prior knowledge of AI was weakly correlated with a reduction of the impact of the bias, highlighting the possible importance of AI education for robust mitigation of bias effects. Our findings not only highlight the critical effects of interacting with biased LLMs and its ability to impact public discourse and political conduct, but also highlights potential techniques for mitigating these risks in the future.

Belchior Ribeiro Leite, Rosa Amélia Pereira da Silva

O artigo tem como objetivo compreender como a valorização da cultura popular local e/ou regional pode contribuir com a formação integral do sujeito. Além disso, visa definir os conceitos de cultura e de cultura popular, identificar traços da cultura popular no contexto da Escola Família Agrícola (EFAN) de Natalândia e perceber a visão do profissional da EFAN acerca da cultura popular local/regional. O estudo se caracteriza como uma pesquisa de campo, enquadrada na abordagem qualitativa. Quanto aos procedimentos, realizou-se uma entrevista semiestruturada, feita por meio da Plataforma Zoom. Realizou-se, também, uma revisão bibliográfica, por meio do levantamento de artigos e livros de renomados autores, como Santaella (2003), Santos (1983), Souza e Pereira (2014), Lopes e Macedo (2011), Pessoa (2018), Benjamin (1996), entre outros que discutem os conceitos de cultura e cultura popular. O conteúdo e os dados foram analisados de forma crítica. Os resultados revelaram que existem inúmeras estratégias pedagógicas para auxiliar os estudantes, em especial os da EFAN, na valorização e aproximação da cultura popular local/regional. Portanto, ao trabalhar com manifestações da cultura popular na escola, propicia-se ao indivíduo desenvolver consciência política, de classe, de pertencimento, de valorização do seu espaço de vivência e de fortalecimento da identidade camponesa.

Sebastian T. Braun, Jan Stuhler

With 70 million dead, World War II remains the most devastating conflict in history. Among the survivors, millions were displaced, returned maimed from the battlefield, or endured years of captivity. We examine the effects of such war exposures on labor market careers, showing that they often become apparent only at certain life stages. While war injuries reduced employment in old age, former prisoners of war prolonged their time in the workforce before retiring. Many displaced workers, especially women, never returned to employment. These responses align with standard life-cycle theory and thus likely hold relevance for other conflicts.

Hayda Josiane Alves

O objetivo deste artigo é debater aspectos, relações e sentidos que limitam a participação de crianças e adolescentes na produção de saberes a partir da educação popular. Para tanto, somamo-nos à “crítica do saber” em torno da questionável neutralidade-objetividade das ciências, seus modos de legitimação e suas fronteiras para a participação infantojuvenil. Conceber as crianças e os adolescentes como seres de existência plena implica importantes desafios, visto que esses sujeitos tendem a ser negligenciados como seres de cultura, saber e educação, além de invisibilizados em escritos acadêmicos sobre suas famílias e suas comunidades. Além disso, nos processos de pesquisa, são subalternizados pela tutela do adulto como parte de uma regulamentação especial dos Comitês de Ética em Pesquisa voltada à proteção desse grupo, mas que acaba por silenciar suas vozes e comprometer sua autonomia. Torna-se fundamental aprofundar esse debate sob a perspectiva das epistemologias críticas e emergentes para além de um eixo ético-legal capaz de vislumbrar crianças e adolescentes como sujeitos plenos. Nesse caminho, as pedagogias freirianas possibilitam reflexões potentes ao reconhecer tais indivíduos como seres humanos em sua totalidade, capazes de criar e transformar saberes necessários à produção de conhecimento democrático.

Daniele Trevisan, Edson Caetano

Este texto se ocupa da reflexão acerca das pesquisas empíricas desenvolvidas no âmbito do projeto de extensão “Conhecimentos tradicionais e o direito de reconhecimento de benzedeiras e benzedores do Quilombo de Mata Cavalo/Nossa Senhora do Livramento/MT”, desenvolvido pelo Grupo de Estudos e Pesquisas sobre Trabalho e Educação da Universidade do Mato Grosso (GEPTE/UFTM). Caracteriza-se como uma pesquisa de cunho qualitativo e metodologia de pesquisa participante na qual buscamos identificar os sentidos possíveis para o uso das mídias digitais no contexto de compartilhamento de práticas tradicionais de cuidado e cura desenvolvidas pelo Coletivo Saberes e Fazeres Curativos do Quilombo de Mata Cavalo. Objetivamos refletir o processo de incorporação e utilização das tecnologias nas práticas no âmbito do projeto, bem como os limites de tal utilização diante dos tempos, espaços e lugares socioculturais em que os sujeitos estão inseridos.

Noah Bergam, Emily Allaway, Kathleen McKeown

We analyze publicly available US Supreme Court documents using automated stance detection. In the first phase of our work, we investigate the extent to which the Court's public-facing language is political. We propose and calculate two distinct ideology metrics of SCOTUS justices using oral argument transcripts. We then compare these language-based metrics to existing social scientific measures of the ideology of the Supreme Court and the public. Through this cross-disciplinary analysis, we find that justices who are more responsive to public opinion tend to express their ideology during oral arguments. This observation provides a new kind of evidence in favor of the attitudinal change hypothesis of Supreme Court justice behavior. As a natural extension of this political stance detection, we propose the more specialized task of legal stance detection with our new dataset SC-stance, which matches written opinions to legal questions. We find competitive performance on this dataset using language adapters trained on legal documents.

Manoel Júnior, Philipe Melo, Daniel Kansaon et al.

Instant messaging platforms such as Telegram became one of the main means of communication used by people all over the world. Most of them are home of several groups and channels that connect thousands of people focused on political topics. However, they have suffered with misinformation campaigns with a direct impact on electoral processes around the world. While some platforms, such as WhatsApp, took restrictive policies and measures to attenuate the issues arising from the abuse of their systems, others have emerged as alternatives, presenting little or no restrictions on content moderation or actions in combating misinformation. Telegram is one of those systems, which has been attracting more users and gaining popularity. In this work, we present the "Telegram Monitor", a web-based system that monitors the political debate in this environment and enables the analysis of the most shared content in multiple channels and public groups. Our system aims to allow journalists, researchers, and fact-checking agencies to identify trending conspiracy theories, misinformation campaigns, or simply to monitor the political debate in this space along the 2022 Brazilian elections. We hope our system can assist the combat of misinformation spreading through Telegram in Brazil.

Luciane Senna Ferreira, Jeandro da Silva Borba

O relato parte da experiência do Projeto Saúde, desenvolvido, em 2019, com o Ensino Médio, pelo Instituto Federal do Rio Grande do Sul, Campus Osório. O objetivo foi realizar atividades de Educação em Saúde, criando uma rede de vínculo entre escola e Secretaria Municipal de Saúde para desenvolver ações direcionadas às demandas enfrentadas no cotidiano escolar. Foi adotada a perspectiva de saúde articulada a questões de gênero e sexualidade, considerando a realidade adolescente. A metodologia privilegiou a oficina, valorizando o protagonismo estudantil como agente de transformação social. O projeto possibilitou a construção do vínculo entre a Secretaria e o Instituto, o fortalecimento estudantil no reconhecimento das situações de vulnerabilidades, para que possam se proteger e se mobilizarem contra elas, e subsídios para futuros projetos de educação em saúde. Algumas dificuldades/limitações ocorreram no decorrer da proposta, como tempo e período de execução, recurso humano e forma de avaliação. O projeto foi relevante por apresentar o tema da saúde relacionado com os de gênero e sexualidade, problematizando-os nas vivências das/dos estudantes e, também, por apontar a importância do vínculo entre setores de saúde e escola para promoção de espaços que trabalhem educação em saúde com as/os adolescentes.