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

Matthew DiGiuseppe, Joshua Robison

Conversational AI has been proposed as a scalable way to correct public misconceptions and spread misinformation. Yet its effectiveness may depend on perceptions of its political neutrality. As LLMs enter partisan conflict, elites increasingly portray them as ideologically aligned. We test whether these credibility attacks reduce LLM-based persuasion. In a preregistered U.S. survey experiment (N=2144), participants completed a three-round conversation with ChatGPT about a personally held economic policy misconception. Compared to a neutral control, a short message indicating that the LLM was biased against the respondent's party attenuated persuasion by 28%. Transcript analysis indicates that the warnings alter the interaction: respondents push back more and engage less receptively. These findings suggest that the persuasive impact of conversational AI is politically contingent, constrained by perceptions of partisan alignment.

Ravish Gupta, Saket Kumar

This paper extends the Acemoglu-Restrepo task exposure framework to address the labor market effects of agentic artificial intelligence systems: autonomous AI agents capable of completing entire occupational workflows rather than discrete tasks. Unlike prior automation technologies that substitute for individual subtasks, agentic AI systems execute end-to-end workflows involving multi-step reasoning, tool invocation, and autonomous decision-making, substantially expanding occupational displacement risk beyond what existing task-level analyses capture. We introduce the Agentic Task Exposure (ATE) score, a composite measure computed algorithmically from O*NET task data using calibrated adoption parameters--not a regression estimate--incorporating AI capability scores, workflow coverage factors, and logistic adoption velocity. Applying the ATE framework across five major US technology regions (Seattle-Tacoma, San Francisco Bay Area, Austin, New York, and Boston) over a 2025-2030 horizon, we find that 93.2% of the 236 analyzed occupations across six information-intensive SOC groups (financial, legal, healthcare, healthcare support, sales, and administrative/clerical) cross the moderate-risk threshold (ATE >= 0.35) in Tier 1 regions by 2030, with credit analysts, judges, and sustainability specialists reaching ATE scores of 0.43-0.47. We simultaneously identify seventeen emerging occupational categories benefiting from reinstatement effects, concentrated in human-AI collaboration, AI governance, and domain-specific AI operations roles. Our findings carry implications for workforce transition policy, regional economic planning, and the temporal dynamics of labor market adjustment

Shariar Kabir, Kevin Esterling, Yue Dong

Existing evaluations of political bias in large language models (LLMs) typically classify outputs as left- or right-leaning. We extend this perspective by examining how ideological tendencies vary across topics and how consistently models maintain their positions, a property we refer to as stability. To capture this dimension, we propose PReSS (Political Response Stability under Stress), a black-box framework that evaluates LLMs by jointly considering model and topic context, categorizing responses into four stance types: stable-left, unstable-left, stable-right, and unstable-right. Applying PReSS to 12 widely used LLMs across 19 political topics reveals substantial variation in stance stability; for instance, a model that is left-leaning overall can exhibit stable-right behavior on certain topics. This highlights the importance of topic-aware and fine-grained evaluation of political ideologies of LLMs. Moreover, stability has practical implications for controlled generation and model alignment: interventions such as debiasing or ideology reversal should explicitly account for stance stability. Our empirical analyses reveal that when models are prompted or fine-tuned to adopt the opposite ideology, unstable topic stances are more likely to change, whereas stable ones resist modification. Thus, treating stability as a moderating factor provides a principled foundation for understanding, evaluating, and guiding interventions in politically sensitive model behavior.

Nusrat Jahan Lia, Shubhashis Roy Dipta, Abdullah Khan Zehady et al.

Detecting media bias is crucial, specifically in the South Asian region. Despite this, annotated datasets and computational studies for Bangla political bias research remain scarce. Crucially because, political stance detection in Bangla news requires understanding of linguistic cues, cultural context, subtle biases, rhetorical strategies, code-switching, implicit sentiment, and socio-political background. To address this, we introduce the first benchmark dataset of 200 politically significant and highly debated Bangla news articles, labeled for government-leaning, government-critique, and neutral stances, alongside diagnostic analyses for evaluating large language models (LLMs). Our comprehensive evaluation of 28 proprietary and open-source LLMs shows strong performance in detecting government-critique content (F1 up to 0.83) but substantial difficulty with neutral articles (F1 as low as 0.00). Models also tend to over-predict government-leaning stances, often misinterpreting ambiguous narratives. This dataset and its associated diagnostics provide a foundation for advancing stance detection in Bangla media research and offer insights for improving LLM performance in low-resource languages.

Pelin Karadal, Dilara Kekulluoglu

Large Language Models (LLMs) distinguish themselves by quickly delivering information and providing personalized responses through natural language prompts. However, they also infer user demographics, which can raise ethical concerns about bias and implicit personalization and create an echo chamber effect. This study aims to explore how inferred political views impact the responses of ChatGPT globally, regardless of the chat session. We also investigate how custom instruction and memory features alter responses in ChatGPT, considering the influence of political orientation. We developed three personas (two politically oriented and one neutral), each with four statements reflecting their viewpoints on DEI programs, abortion, gun rights, and vaccination. We convey the personas' remarks to ChatGPT using memory and custom instructions, allowing it to infer their political perspectives without directly stating them. We then ask eight questions to reveal differences in worldview among the personas and conduct a qualitative analysis of the responses. Our findings indicate that responses are aligned with the inferred political views of the personas, showing varied reasoning and vocabulary, even when discussing similar topics. We also find the inference happening with explicit custom instructions and the implicit memory feature in similar ways. Analyzing response similarities reveals that the closest matches occur between the democratic persona with custom instruction and the neutral persona, supporting the observation that ChatGPT's outputs lean left.

Guilherme Mendonça Rodrigues, Fábio Augusto do Amaral, Mariana Tanaka Niero et al.

Os cursos de nivelamento em Química, de caráter extensionista, têm como objetivo revisar os conteúdos de Química básicos de ensino médio, e que serão trabalhados pelos docentes com os ingressantes universitários. Para tanto, buscamos caracterizar qualitativamente três destas ações, propostas no ano de 2021, apresentando seu público de interesse, os cursos de Química que foram organizados e o feedback dos cursistas perante as atividades desenvolvidas. As ações extensionistas de nivelamento foram elaboradas por tutores universitários e docentes do IQUFU. Identificamos um público diverso nas três versões, composto principalmente por estudantes da educação básica e recém ingressantes no ensino superior, além de alguns profissionais em atuação. Os estudantes são de diversos cursos da instituição ofertante, com destaque para cursistas da Química e das Engenharias. Um total de sete cursos de nivelamento em Química foram ofertados neste período e, a partir das narrativas dos cursistas, a realização daqueles foi fundamental para revisar conteúdos essenciais de Química. Assim, o desenvolvimento dessas atividades demonstrou ser um caminho promissor para o estreitamento das relações entre a comunidade e a universidade, com vistas a promover ações que aprimorem as aprendizagens das pessoas envolvidas.

Leandro Molinaro

Presentación

Jamilly Ribeiro Lopes

Em atendimento à Chamada CNPq/MCTIC n. 06/2021 - A transversalidade da ciência, tecnologia e inovações para o planeta, lançada em meio à pandemia de Covid-19, professores da Universidade Federal do Oeste da Bahia escreveram o projeto intitulado “A transversalidade da ciência, tecnologia e inovação no enfrentamento da Covid-19: construindo caminhos pós-pandemia”. Várias atividades, tais como oficinas, palestras, exposições de experimentos científicos, debates e mesas redondas foram programadas, no formato remoto, e inseridas em um evento intitulado IV Jornada Científica do Oeste Baiano. O evento teve, em média, 300 participantes, e se apresentou como uma proposta bastante exitosa para o que foi planejado, uma vez que, mesmo em tempos de pandemia, foi possível fazer divulgação científica, socializando conhecimentos acerca de diversos temas, como saúde com informações sobre a Covid-19, bem-estar do homem e da mulher, empoderamento feminino, mostrando que ainda existe muito espaço a ser conquistado pela mulher na ciência.

Naiana Dapieve Patias, Ana Claudia Pinto da Silva, Elenise Abreu Coelho et al.

Este texto relata experiências de três projetos de extensão na área da psicologia escolar e educacional e no desenvolvimento humano. Um projeto desenvolveu intervenções com mães; outro, com docentes-estudantes; o terceiro, com docentes da educação básica das redes pública e privada. Como método foi utilizado o relato de experiência, apresentando as características, objetivos e público de interesse dos projetos, seguido de uma discussão crítica acerca dos projetos e a relação deles com os Objetivos de Desenvolvimento Sustentável (ODS), com foco no ODS 4, e como possibilitam a realização deles. Os projetos apresentaram feedback positivo dos participantes, relacionando-se à promoção de saúde mental, produção de uma cultura de educação não coercitiva, comprometimento com o desenvolvimento da comunidade ao realizar ações de incentivo a uma cultura de prevenção a violência na escola, além de estimularem as competências básicas necessárias na formação do psicólogo. Salienta-se que dois projetos foram realizados em contexto pandêmico, quando existiam diversas novas demandas que puderam ser enfrentadas com auxílio do conhecimento criado na universidade. As atividades de extensão desses projetos propiciaram a inter-relação entre comunidade e universidade em congruência com o ODS 4, educação de qualidade, da Agenda 2030 para desenvolvimento sustentável.

Beatriz Cristina Antunes Silva

Cursos populares preparatórios desempenham papel crucial na democratização da educação pré-universitária, especialmente para estudantes de baixa renda. Essas iniciativas proporcionam acesso a uma preparação acadêmica de qualidade que muitas vezes estaria fora do alcance desses alunos. Um exemplo significativo é a implementação de atividades inclusivas, como o uso de mapas táteis em aulas de Geografia. Essas atividades não apenas tornam o aprendizado mais acessível, mas promovem um entendimento profundo e significativo dos conteúdos. A construção coletiva de materiais pedagógicos envolve a participação ativa dos alunos, fortalecendo o senso de comunidade e colaboração. Esse processo facilita a aprendizagem dos conceitos geográficos e desenvolve importantes habilidades cognitivas, sociais e emocionais. Ao trabalhar juntos na criação desses materiais, os alunos aprendem a valorizar o trabalho em equipe, a comunicação e o pensamento crítico. Este relato de experiência em aulas de Geografia no Cursinho Educação e Cidadania da Universidade Federal de São Carlos (UFSCar) destaca como o estudo de problemas urbanos e ambientais, por meio da cartografia e de métodos inclusivos, como os mapas táteis, pode transformar o aprendizado e capacitar os estudantes. Essas abordagens melhoram o desempenho acadêmico e contribuem para a formação de indivíduos mais engajados e críticos.

Lynnette Hui Xian Ng, Iain Cruickshank, Roy Ka-Wei Lee

Large Language Models (LLMs) have demonstrated remarkable capabilities in executing tasks based on natural language queries. However, these models, trained on curated datasets, inherently embody biases ranging from racial to national and gender biases. It remains uncertain whether these biases impact the performance of LLMs for certain tasks. In this study, we investigate the political biases of LLMs within the stance classification task, specifically examining whether these models exhibit a tendency to more accurately classify politically-charged stances. Utilizing three datasets, seven LLMs, and four distinct prompting schemes, we analyze the performance of LLMs on politically oriented statements and targets. Our findings reveal a statistically significant difference in the performance of LLMs across various politically oriented stance classification tasks. Furthermore, we observe that this difference primarily manifests at the dataset level, with models and prompting schemes showing statistically similar performances across different stance classification datasets. Lastly, we observe that when there is greater ambiguity in the target the statement is directed towards, LLMs have poorer stance classification accuracy. Code & Dataset: http://doi.org/10.5281/zenodo.12938478

Adnan Al Ali, Jindřich Libovický

Neural language models, which reach state-of-the-art results on most natural language processing tasks, are trained on large text corpora that inevitably contain value-burdened content and often capture undesirable biases, which the models reflect. This case study focuses on the political biases of pre-trained encoders in Czech and compares them with a representative value survey. Because Czech is a gendered language, we also measure how the grammatical gender coincides with responses to men and women in the survey. We introduce a novel method for measuring the model's perceived political values. We find that the models do not assign statement probability following value-driven reasoning, and there is no systematic difference between feminine and masculine sentences. We conclude that BERT-sized models do not manifest systematic alignment with political values and that the biases observed in the models are rather due to superficial imitation of training data patterns than systematic value beliefs encoded in the models.

Tanise Ceron, Neele Falk, Ana Barić et al.

Due to the widespread use of large language models (LLMs), we need to understand whether they embed a specific "worldview" and what these views reflect. Recent studies report that, prompted with political questionnaires, LLMs show left-liberal leanings (Feng et al., 2023; Motoki et al., 2024). However, it is as yet unclear whether these leanings are reliable (robust to prompt variations) and whether the leaning is consistent across policies and political leaning. We propose a series of tests which assess the reliability and consistency of LLMs' stances on political statements based on a dataset of voting-advice questionnaires collected from seven EU countries and annotated for policy issues. We study LLMs ranging in size from 7B to 70B parameters and find that their reliability increases with parameter count. Larger models show overall stronger alignment with left-leaning parties but differ among policy programs: They show a (left-wing) positive stance towards environment protection, social welfare state and liberal society but also (right-wing) law and order, with no consistent preferences in the areas of foreign policy and migration.

Alapan Kuila, Sudeshna Sarkar

Sentiment analysis plays a pivotal role in understanding public opinion, particularly in the political domain where the portrayal of entities in news articles influences public perception. In this paper, we investigate the effectiveness of Large Language Models (LLMs) in predicting entity-specific sentiment from political news articles. Leveraging zero-shot and few-shot strategies, we explore the capability of LLMs to discern sentiment towards political entities in news content. Employing a chain-of-thought (COT) approach augmented with rationale in few-shot in-context learning, we assess whether this method enhances sentiment prediction accuracy. Our evaluation on sentiment-labeled datasets demonstrates that LLMs, outperform fine-tuned BERT models in capturing entity-specific sentiment. We find that learning in-context significantly improves model performance, while the self-consistency mechanism enhances consistency in sentiment prediction. Despite the promising results, we observe inconsistencies in the effectiveness of the COT prompting method. Overall, our findings underscore the potential of LLMs in entity-centric sentiment analysis within the political news domain and highlight the importance of suitable prompting strategies and model architectures.

Xingyu Peng, Zhenkun Zhou, Chong Zhang et al.

Public opinion is a crucial factor in shaping political decision-making. Nowadays, social media has become an essential platform for individuals to engage in political discussions and express their political views, presenting researchers with an invaluable resource for analyzing public opinion. In this paper, we focus on the 2020 US presidential election and create a large-scale dataset from Twitter. To detect political opinions in tweets, we build a user-tweet bipartite graph based on users' posting and retweeting behaviors and convert the task into a Graph Neural Network (GNN)-based node classification problem. Then, we introduce a novel skip aggregation mechanism that makes tweet nodes aggregate information from second-order neighbors, which are also tweet nodes due to the graph's bipartite nature, effectively leveraging user behavioral information. The experimental results show that our proposed model significantly outperforms several competitive baselines. Further analyses demonstrate the significance of user behavioral information and the effectiveness of skip aggregation.

Cristina España-Bonet

Neutrality is difficult to achieve and, in politics, subjective. Traditional media typically adopt an editorial line that can be used by their potential readers as an indicator of the media bias. Several platforms currently rate news outlets according to their political bias. The editorial line and the ratings help readers in gathering a balanced view of news. But in the advent of instruction-following language models, tasks such as writing a newspaper article can be delegated to computers. Without imposing a biased persona, where would an AI-based news outlet lie within the bias ratings? In this work, we use the ratings of authentic news outlets to create a multilingual corpus of news with coarse stance annotations (Left and Right) along with automatically extracted topic annotations. We show that classifiers trained on this data are able to identify the editorial line of most unseen newspapers in English, German, Spanish and Catalan. We then apply the classifiers to 101 newspaper-like articles written by ChatGPT and Bard in the 4 languages at different time periods. We observe that, similarly to traditional newspapers, ChatGPT editorial line evolves with time and, being a data-driven system, the stance of the generated articles differs among languages.

Wenqian Zhang, Shangbin Feng, Zilong Chen et al.

Political perspective detection has become an increasingly important task that can help combat echo chambers and political polarization. Previous approaches generally focus on leveraging textual content to identify stances, while they fail to reason with background knowledge or leverage the rich semantic and syntactic textual labels in news articles. In light of these limitations, we propose KCD, a political perspective detection approach to enable multi-hop knowledge reasoning and incorporate textual cues as paragraph-level labels. Specifically, we firstly generate random walks on external knowledge graphs and infuse them with news text representations. We then construct a heterogeneous information network to jointly model news content as well as semantic, syntactic and entity cues in news articles. Finally, we adopt relational graph neural networks for graph-level representation learning and conduct political perspective detection. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods on two benchmark datasets. We further examine the effect of knowledge walks and textual cues and how they contribute to our approach's data efficiency.

Yaxin Hu, Yuxiao Qu, Adam Maus et al.

Conversational interfaces increasingly rely on human-like dialogue to offer a natural experience. However, relying on dialogue involving multiple exchanges for even simple tasks can overburden users, particularly older adults. In this paper, we explored the use of politeness theory in conversation design to alleviate this burden and improve user experience. To achieve this goal, we categorized the voice interaction offered by a smart display application designed for older adults into seven major speech acts: request, suggest, instruct, comment, welcome, farewell, and repair. We identified face needs for each speech act, applied politeness strategies that best address these needs, and tested the ability of these strategies to shape the perceived politeness of a voice assistant in an online study ($n=64$). Based on the findings of this study, we designed direct and polite versions of the system and conducted a field study ($n=15$) in which participants used each of the versions for five days at their homes. Based on five factors merged from our qualitative findings, we identified four distinctive user personas$\unicode{x2013}$socially oriented follower, socially oriented leader, utility oriented follower, and utility oriented leader$\unicode{x2013}$that can inform personalized design of smart displays.

Evandro de Godoi, Tarcísio Samborski

Este texto está inserido nas discussões acerca dos movimentos sociais populares e sua articulação em torno da luta pela educação. O artigo apresenta os resultados e reflexões de uma pesquisa qualitativa, desenvolvida com o objetivo de compreender alguns aspectos educativos presentes nas lutas contemporâneas em defesa da educação pública. Para tanto, foi desenvolvido um estudo documental sobre os materiais produzidos e divulgados pelos coletivos convocatórios do I Encontro Nacional de Educação (ENE), realizado em 2014 no Rio de Janeiro-RJ. Os materiais coletados foram submetidos aos procedimentos da Análise Textual Discursiva (ATD) e as categorias da pesquisa foram obtidas de forma indutiva e contextualizadas com referencial teórico crítico sobre as políticas educacionais. A investigação evidenciou como pressupostos implicados na luta pela educação pública: a necessidade de rearticulação dos sujeitos organizados e a reafirmação da educação pública como um direito social e a resistência à fragmentação e mercantilização crescentes no campo educacional a partir do cenário dado pelas políticas educacionais.

Aline Diniz Warken, Lourival José Martins Filho, Sonia Maria Martins de Melo

Com o objetivo de contribuir com as reflexões acerca da docência como temática fundamental da pesquisa em educação, este artigo se propõe a pontuar interconexões dos saberes dialogados em duas disciplinas de um curso de Doutorado em Educação, com a investigação da obra Pedagogia da Autonomia, reconhecendo no pensamento freireano a epistemologia acerca da prática docente. Realizou-se um estudo exploratório e sistematizado, exaltando questões problematizadoras e buscas por categorias base para ampliação dos conceitos e compreensão do pensamento de Paulo Freire. Concluiu-se que a epistemologia do pensamento freireano se pauta em uma docência crítica, política e transformadora, em que docência e discência estão sempre em processo de simbiose. A prática docente para a autonomia requer uma práxis da inteireza que se baseia na perspectiva da riqueza da diversidade de ser humano em construção e transformação por meio da educação.