A central socioeconomic concern about Artificial Intelligence is that it will lower wages by depressing the labor share - the fraction of economic output paid to labor. We show that declining labor share is more likely to raise wages. In a competitive economy with constant returns to scale, we prove that the wage-maximizing labor share depends only on the capital-to-labor ratio, implying a non-monotonic relationship between labor share and wages. When labor share exceeds this wage-maximizing level, further automation increases wages even while reducing labor's output share. Using data from the United States and eleven other industrialized countries, we estimate that labor share is too high in all twelve, implying that further automation should raise wages. Moreover, we find that falling labor share accounted for 16\% of U.S. real wage growth between 1954 and 2019. These wage gains notwithstanding, automation-driven shifts in labor share are likely to pose significant social and political challenges.
Felix Anand Epp, Matti Nelimarkka, Jesse Haapoja
et al.
This is the Proceedings of the First CHI Workshop on CHIdeology: Disentangling the fragmented politics, values, and imaginaries of Human-Computer Interaction through ideologies, held on Wednesday, 15 April, in Barcelona, Spain, at the ACM CHI Conference on Human Factors in Computing Systems.
Lorcan McLaren, James Cross, Zuzanna Krakowska
et al.
Political scientists are rapidly adopting large language models (LLMs) for text annotation, yet the sensitivity of annotation results to implementation choices remains poorly understood. Most evaluations test a single model or configuration; how model choice, model size, learning approach, and prompt style interact, and whether popular "best practices" survive controlled comparison, are largely unexplored. We present a controlled evaluation of these pipeline choices, testing six open-weight models across four political science annotation tasks under identical quantisation, hardware, and prompt-template conditions. Our central finding is methodological: interaction effects dominate main effects, so seemingly reasonable pipeline choices can become consequential researcher degrees of freedom. No single model, prompt style, or learning approach is uniformly superior, and the best-performing model varies across tasks. Two corollaries follow. First, model size is an unreliable guide both to cost and to performance: cross-family efficiency differences are so large that some larger models are less resource-intensive than much smaller alternatives, while within model families mid-range variants often match or exceed larger counterparts. Second, widely recommended prompt engineering techniques yield inconsistent and sometimes negative effects on annotation performance. We use these benchmark results to develop a validation-first framework - with a principled ordering of pipeline decisions, guidance on prompt freezing and held-out evaluation, reporting standards, and open-source tools - to help researchers navigate this decision space transparently.
Fynn Bachmann, Daan van der Weijden, Lucien Heitz
et al.
Adaptive questionnaires dynamically select the next question for a survey participant based on their previous answers. Due to digitalisation, they have become a viable alternative to traditional surveys in application areas such as political science. One limitation, however, is their dependency on data to train the model for question selection. Often, such training data (i.e., user interactions) are unavailable a priori. To address this problem, we (i) test whether Large Language Models (LLM) can accurately generate such interaction data and (ii) explore if these synthetic data can be used to pre-train the statistical model of an adaptive political survey. To evaluate this approach, we utilise existing data from the Swiss Voting Advice Application (VAA) Smartvote in two ways: First, we compare the distribution of LLM-generated synthetic data to the real distribution to assess its similarity. Second, we compare the performance of an adaptive questionnaire that is randomly initialised with one pre-trained on synthetic data to assess their suitability for training. We benchmark these results against an "oracle" questionnaire with perfect prior knowledge. We find that an off-the-shelf LLM (GPT-4) accurately generates answers to the Smartvote questionnaire from the perspective of different Swiss parties. Furthermore, we demonstrate that initialising the statistical model with synthetic data can (i) significantly reduce the error in predicting user responses and (ii) increase the candidate recommendation accuracy of the VAA. Our work emphasises the considerable potential of LLMs to create training data to improve the data collection process in adaptive questionnaires in LLM-affine areas such as political surveys.
Kobi Hackenburg, Ben M. Tappin, Luke Hewitt
et al.
There are widespread fears that conversational AI could soon exert unprecedented influence over human beliefs. Here, in three large-scale experiments (N=76,977), we deployed 19 LLMs-including some post-trained explicitly for persuasion-to evaluate their persuasiveness on 707 political issues. We then checked the factual accuracy of 466,769 resulting LLM claims. Contrary to popular concerns, we show that the persuasive power of current and near-future AI is likely to stem more from post-training and prompting methods-which boosted persuasiveness by as much as 51% and 27% respectively-than from personalization or increasing model scale. We further show that these methods increased persuasion by exploiting LLMs' unique ability to rapidly access and strategically deploy information and that, strikingly, where they increased AI persuasiveness they also systematically decreased factual accuracy.
This paper examines a novel proxy for political polarization, initially proposed by Caliskan et al., which estimates intergroup distances using computer vision. Analyzing 1,400+ YouTube videos with advanced object detection, their study quantifies demographic and religious divides in Turkiye, a deeply polarized nation. Our findings reveal strong correlations between intergroup distances and electoral polarization, measured via entropy-based voting metrics weighted by religiosity and political inclination. Two key insights emerge: (1) Greater distances between religious and nonreligious individuals (NRP vs RP) heighten electoral entropy, underscoring sociocultural fragmentation. (2) Intragroup diversity among nonreligious individuals (NRP vs NRP) stabilizes polarization, aligning with Axelrod's cultural dissemination model. This research advances computational social science and economics by showing that physical distancing serves as a scalable proxy for polarization, complementing traditional economic indicators.
An individual's opinion concerning political bias in the media is shaped by exogenous factors (independent analysis of media outputs) and endogenous factors (social activity, e.g. peer pressure by political allies and opponents in a network). Previous numerical studies show, that persuadable agents in allies-only networks are disrupted from asymptotically learning the intrinsic bias of a media organization, when the network is populated by one or more obdurate agents (partisans), who are not persuadable themselves but exert peer pressure on other agents. Some persuadable agents asymptotically learn a false bias, while others vacillate indefinitely between a false bias and the true bias, a phenomenon called turbulent nonconvergence which also emerges in opponents-only and mixed networks without partisans. Here we derive an analytic instability condition, which demarcates turbulent nonconvergence from asymptotic learning as a function of key network properties, for an idealized model of media bias featuring a biased coin. The condition is verified with Monte Carlo simulations as a function of network size, sparsity, and partisan fraction. It is derived in a probabilistic framework, where an agent's opinion is uncertain and is described by a probability density function, which is multimodal in general, generalizing previous studies which assume that an agent's opinion is certain (i.e. described by one number). The results and their social implications are interpreted briefly in terms of the social science theory of structural balance.
We investigate the impact of politeness levels in prompts on the performance of large language models (LLMs). Polite language in human communications often garners more compliance and effectiveness, while rudeness can cause aversion, impacting response quality. We consider that LLMs mirror human communication traits, suggesting they align with human cultural norms. We assess the impact of politeness in prompts on LLMs across English, Chinese, and Japanese tasks. We observed that impolite prompts often result in poor performance, but overly polite language does not guarantee better outcomes. The best politeness level is different according to the language. This phenomenon suggests that LLMs not only reflect human behavior but are also influenced by language, particularly in different cultural contexts. Our findings highlight the need to factor in politeness for cross-cultural natural language processing and LLM usage.
Serina Chang, Alicja Chaszczewicz, Emma Wang
et al.
Generating social networks is essential for many applications, such as epidemic modeling and social simulations. The emergence of generative AI, especially large language models (LLMs), offers new possibilities for social network generation: LLMs can generate networks without additional training or need to define network parameters, and users can flexibly define individuals in the network using natural language. However, this potential raises two critical questions: 1) are the social networks generated by LLMs realistic, and 2) what are risks of bias, given the importance of demographics in forming social ties? To answer these questions, we develop three prompting methods for network generation and compare the generated networks to a suite of real social networks. We find that more realistic networks are generated with "local" methods, where the LLM constructs relations for one persona at a time, compared to "global" methods that construct the entire network at once. We also find that the generated networks match real networks on many characteristics, including density, clustering, connectivity, and degree distribution. However, we find that LLMs emphasize political homophily over all other types of homophily and significantly overestimate political homophily compared to real social networks.
Women are underrepresented in many areas of journalistic newsrooms. In this paper, we examine if this established effect continues in the new forms of journalistic communication, Social Media Networks. We used mentions, retweets, and hashtags as journalistic amplification and legitimation measures. Furthermore, we compared two groups of journalists in different stages of development: political and data journalists in Germany in 2021. Our results show that journalists regarded as women tend to favor their other women in mentions and retweets on Twitter, compared to men. While both professions are dominated by many men and a high share of men-authored tweets, women are mentioning and retweeting other women to a more extensive degree than their male colleagues. Women data journalists also leveraged different sources than men. In addition, we have found data journalists to be more inclusive towards non-member sources in their network compared to political journalists.
Josiléia Curty de Oliveira, Rainei Rodrigues Jadejiski, Ozana Luzia Galvão Baldotto
et al.
Este texto traz experiências formativas de professoras do campo do sul do estado do Espírito Santo que participaram do curso de Especialização em Educação do Campo – Escola da Terra Capixaba. As ações dessa formação continuada estiveram ligadas à atuação do grupo de pesquisa CNPq Culturas, Parcerias e Educação do Campo, da Universidade Federal do Espírito Santo, e foram referenciadas nos pressupostos da educação do campo, com foco na epistemologia da práxis como base para a construção de conhecimentos e de uma formação crítica e emancipadora. A metodologia do estudo se ancora nos pressupostos da pesquisa narrativa (auto)biográfica, tendo como colaboradoras as professoras que atuam nos anos iniciais do ensino fundamental. Os resultados revelam que o processo formativo possibilitou que as professoras repensassem a própria prática, tornando-as mais sensíveis à realidade dos estudantes campesinos e contribuindo para amenizar a lacuna dos estudos sobre educação do campo existente entre a formação inicial e a continuada.
Social Sciences, Labor in politics. Political activity of the working class
Shreya Havaldar, Matthew Pressimone, Eric Wong
et al.
Understanding how styles differ across languages is advantageous for training both humans and computers to generate culturally appropriate text. We introduce an explanation framework to extract stylistic differences from multilingual LMs and compare styles across languages. Our framework (1) generates comprehensive style lexica in any language and (2) consolidates feature importances from LMs into comparable lexical categories. We apply this framework to compare politeness, creating the first holistic multilingual politeness dataset and exploring how politeness varies across four languages. Our approach enables an effective evaluation of how distinct linguistic categories contribute to stylistic variations and provides interpretable insights into how people communicate differently around the world.
Christoph Börgers, Bruce Boghosian, Natasa Dragovic
et al.
Political candidates often shift their positions opportunistically in hopes of capturing more votes. When there are only two candidates, the best strategy for each of them is often to move towards the other. This eventually results in two centrists with coalescing views. However, the strategy of moving towards the other candidate ceases to be optimal when enough voters abstain instead of voting for a centrist who does not represent their views. These observations, formalized in various ways, have been made many times. Our own formalization is based on differential equations. The surprise and main result derived from these equations is that the final candidate positions can jump discontinuously as the voters' loyalty towards their candidate wanes. The underlying mathematical mechanism is a blue sky bifurcation.
AbstractIn the decade since International Labor and Working-Class History (ILWCH) published its special issue on “Labor and the Military,” treating military service as a problem of labor has grown from a provocation into a major debate. By surveying five recent books on soldiering as a form of labor, this essay poses a set of questions about warfare and work. Is military service best understood as a form of labor, and what might that perspective reveal, or occlude? How do militaries draw the line between those who work and those who fight? Where does that line become blurry? How do soldiers themselves understand the peculiar forms of “work” that war demands? War and work are not separate domains of experience, as these books show. But in some respects, they still demand different tools of analysis.
Flora Sakketou, Joan Plepi, Riccardo Cervero
et al.
Proactively identifying misinformation spreaders is an important step towards mitigating the impact of fake news on our society. In this paper, we introduce a new contemporary Reddit dataset for fake news spreader analysis, called FACTOID, monitoring political discussions on Reddit since the beginning of 2020. The dataset contains over 4K users with 3.4M Reddit posts, and includes, beyond the users' binary labels, also their fine-grained credibility level (very low to very high) and their political bias strength (extreme right to extreme left). As far as we are aware, this is the first fake news spreader dataset that simultaneously captures both the long-term context of users' historical posts and the interactions between them. To create the first benchmark on our data, we provide methods for identifying misinformation spreaders by utilizing the social connections between the users along with their psycho-linguistic features. We show that the users' social interactions can, on their own, indicate misinformation spreading, while the psycho-linguistic features are mostly informative in non-neural classification settings. In a qualitative analysis, we observe that detecting affective mental processes correlates negatively with right-biased users, and that the openness to experience factor is lower for those who spread fake news.
Este texto constitui-se de propostas de oficinas e atividades pedagógicas, elaboradas como recurso prático de uma dissertação de Mestrado Profissional em Educação e Docência. O objetivo é apresentar maneiras e possibilidades de aproximação entre sujeitos e atores diretamente envolvidos nos processos educativos escolares de crianças e de adolescentes, bem como propiciar ações do cotidiano escolar que contribuam com a melhoria da aprendizagem, do comportamento de indisciplina e compartilhar os resultados da nossa pesquisa com as escolas e instituições que atendem crianças e adolescentes. Para tanto, elaboramos um caderno com 4 oficinas que foram realizadas, ora no decorrer da pesquisa de mestrado, ora na sala de aula em que atuávamos, um instrumento que pode servir como um material de apoio das atividades pedagógicas. Por meio das oficinas, os alunos da rede estadual e da rede municipal do ensino fundamental começaram a ter relações mais respeitosas, de cooperação e de ajuda mútua. A indisciplina diminuiu, a infrequência teve baixa considerável, eles estão mais comprometidos e motivados a fazerem as atividades, isso se reflete nos resultados das atividades. Pensamos que essas práticas pedagógicas afetaram de modo relevante na trajetória escolar desses alunos, uma vez que também continuamos a acompanhá-los após nossa pesquisa.
Social Sciences, Labor in politics. Political activity of the working class
Daniele Cristine Nickel, João Felipe Capioto Seelent
O projeto de Orientação e Desenvolvimento Pessoal e Profissional tem como objetivo proporcionar orientações e apoio aos alunos do Setor de Educação Profissional e Tecnológica da Universidade Federal do Paraná e à comunidade em geral, no que diz respeito às questões relacionais, pessoais e profissionais que estejam interferindo no bem-estar biopsicossocial. O projeto visa auxiliar o trabalho desenvolvido pelo Núcleo de Orientação Acadêmica de forma a ampliar o campo de intervenção, uma vez que ele possui um enfoque voltado para as questões educacionais de ensino e aprendizagem. A metodologia adotada envolveu a realização de atividades individuais de orientação e apoio. Os instrumentos e as técnicas utilizadas envolveram a aplicação de testes psicológicos para diagnóstico, entrevistas clínicas, vivências e atividades de sensibilização. As ações desse projeto implicam no bem-estar biopsicossocial dos alunos e da comunidade, de forma a refletir na melhoria dos relacionamentos interpessoais tanto no âmbito pessoal como profissional, redução de faltas excessivas, reprovações, desistências e abandono dos cursos.
Social Sciences, Labor in politics. Political activity of the working class
O estudo analisa o estágio curricular supervisionado em espaços não escolares e suas contribuições para formação inicial de pedagogos. Partimos da seguinte questão: qual a contribuição formativa que o estágio curricular supervisionado em espaços não escolares possibilita aos estudantes do Curso de Pedagogia da Universidade do Estado da Bahia? Apresentamos como objetivos de investigação: identificar a articulação entre o currículo, a cultura e a formação no estágio nesses espaços; analisar se o estágio em espaços não escolares tem contribuído com a formação dos acadêmicos. A investigação se inscreve dentro dos princípios da pesquisa qualitativa, desenvolvida em duas fases interdependentes. A primeira composta por uma pesquisa bibliográfica e análise de documentos sobre o referido campo de estudo; a segunda se constituiu numa investigação do fenômeno, na qual se busca resgatar a visão dos sujeitos envolvidos no processo de formação. Os sujeitos desse trabalho foram 30 estudantes de pedagogia e cinco docentes coordenadores do estágio curricular. No olhar dos sujeitos pesquisados o estágio em espaços não escolares mostrou-se como importante campo para ampliação e atuação do profissional pedagogo; espaço de construção da formação e humanização; também explicitou o distanciamento entre a teoria e a prática no desenvolvimento de suas ações.
Social Sciences, Labor in politics. Political activity of the working class