Abstract Labor history has for a long time struggled with so-called “informal” labor, which is situated outside of regularised labor relations, but is widespread in many regions of the globe. The essay reviews five recent books from different fields on transport and labor in Africa, which explore the question of informality, everyday labor, labor organisation, and the infrastructure and technology of mobility. It develops an approach to informal labor that emphasizes historicity and a dialectical model between the stability of the transport infrastructure and the precarity of the workers that uphold it.
Given the significant influence of lawmakers' political ideologies on legislative decision-making, analyzing their impact on transportation-related policymaking is of critical importance. This study introduces a novel framework that integrates a large language model (LLM) with explainable artificial intelligence (XAI) to analyze transportation-related legislative proposals. Legislative bill data from South Korea's 21st National Assembly were used to identify key factors shaping transportation policymaking. These include political affiliations and sponsor characteristics. The LLM was employed to classify transportation-related bill proposals through a stepwise filtering process based on keywords, sentences, and contextual relevance. XAI techniques were then applied to examine the relationships between political party affiliation and associated attributes. The results revealed that the number and proportion of conservative and progressive sponsors, along with district size and electoral population, were critical determinants shaping legislative outcomes. These findings suggest that both parties contributed to bipartisan legislation through different forms of engagement, such as initiating or supporting proposals. This integrated approach offers a valuable tool for understanding legislative dynamics and guiding future policy development, with broader implications for infrastructure planning and governance.
This study examines how political engagement shapes public attitudes toward legal immigration in the United States. Using nationally weighted data from the 2024 ANES Pilot Study, we construct a novel Political Engagement Index (PAX) based on five civic actions: discussing politics, online sharing, attending rallies, wearing political symbols, and campaign volunteering. By applying weighted ordered logistic regression models, we find that higher engagement predicts greater support for easing legal immigration, even after adjusting for education, gender, age, partisanship, income, urban residence, and generalized social trust. To capture the substantive effect, we visualize predicted probabilities across levels of engagement. In full-sample models, the likelihood of supporting "a lot harder" immigration drops from 26% to 13% as engagement rises, while support for "a lot easier" increases from 10% to 21%. Subgroup analyses by partisanship show consistent directionality, with notable shifts among Republicans. Social trust and education are also consistently associated with more open attitudes, while older respondents tend to support less lenient pathways to legal immigration policies. These findings suggest that a cumulative increase in political participation is linked to support for legal immigration pathways, with varying intensity across partisan identities and socio-demographic characteristics.
In an era where social media platforms are central to political communication, the activity of bots raises pressing concerns about amplification, manipulation, and misinformation. Drawing on more than 315 million tweets posted from August 2018 to June 2022, we examine behavioural patterns, sentiment dynamics, and the thematic focus of bot- versus human-generated content spanning the 2018 Brazilian presidential election and the lead-up to the 2022 contest. Our analysis shows that bots relied disproportionately on retweets and replies, with reply activity spiking after the 2018 election, suggesting tactics of conversational infiltration and amplification. Sentiment analysis indicates that bots maintained a narrower emotional tone, in contrast to humans, whose sentiment fluctuated more strongly with political events. Topic modelling further reveals bots' repetitive, Bolsonaro-centric messaging, while human users engaged with a broader range of candidates, civic concerns, and personal reflections. These findings underscore bots' role as amplifiers of narrow agendas and their potential to distort online political discourse.
During the 2022 French presidential election, we collected daily Twitter messages on key topics posted by political candidates and their close networks. Using a data-driven approach, we analyze interactions among political parties, identifying central topics that shape the landscape of political debate. Moving beyond traditional correlation analyses, we apply a causal inference technique: Convergent Cross Mapping, to uncover directional influences among political communities, revealing how some parties are more likely to initiate changes in activity while others tend to respond. This approach allows us to distinguish true influence from mere correlation, highlighting asymmetric relationships and hidden dynamics within the social media political network. Our findings demonstrate how specific issues, such as health and foreign policy, act as catalysts for cross-party influence, particularly during critical election phases. These insights provide a novel framework for understanding political discourse dynamics and have practical implications for campaign strategists and media analysts seeking to monitor and respond to shifts in political influence in real time.
David Sabin-Miller, Mary McGrath, Marisa C. Eisenberg
The concept of ``ideology" is central to political discourse and dynamics, and is often cast as falling primarily on a one-dimensional scale from ``left-wing/liberal" to ``right-wing/conservative", but the validity of this simple quantitative treatment is uncertain. Here we investigate and compare various high-resolution measures of ideology, both internal (individuals self-identification and policy-stance agreements) and external (estimating the ideological position of political opinion statements). We find strong consistency between internal measures, although policy-stance agreement ideology yields a systematically centralizing and liberalizing portrait relative to subjective measures. More remarkably, we find that external assessments of ideology, while noisy, are largely consistent across observers, even for highly dissonant ideas and regardless of speaker identity markers. This supports the use of these responses as meaningful, comparable quantities, which general members of the public reliably project from the abstract space of political thought onto a shared one-dimensional domain. We end with observation of some broad initial patterns of political opinion acceptance, feelings towards the major political parties, and propensity for extreme thinking, finding mostly ideologically symmetric results strongly stratified by strong/lean/Independent political party identity. We hope these perspectives on quantification of political ideology serve as an invitation for a broader range of quantitative scientists to model and understand this vital societal arena.
Autonomía, burocratización y peronismo. Un documento de la CGT (1949) y un texto inédito de Juan Carlos Torre para Pasado y Presente (1974). El documento da cuenta de los debates transcurridos en el Comité Central Confederal (CCC) de la CGT, a propósito de sus estatutos, de diciembre de 1949. En los primeros meses de 1974 Torre transcribió el acta con los intercambios que en aquella reunión desenvolvieron varios dirigentes gremiales, que giraron en torno a la capacidad de intervención de la central en sus organizaciones integrantes. El documento completo no se había difundido y el texto de Torre permaneció inédito. Ahora, ambos materiales se publican en Archivos de historia del movimiento obrero y la izquierda.
1789-, Labor in politics. Political activity of the working class
Rayana Andrade de Carvalho, Camila de Lourdes Cavalcanti Paiva
Este artigo é um relato de experiência de uma proposta pedagógica de educação popular do campo para a educação infantil, desenvolvida a partir de um eixo temático denominado “O que eu posso aprender?”. O projeto pedagógico foi realizado em um Centro Integrado de Educação Infantil (CIEI), localizado na área rural do município Santa Rita/PB, na comunidade de Bebelândia. Utilizou-se a pesquisa como método, a partir da concepção freiriana. Como tema abordado esteve o manguezal, bioma escolhido por estar inserido na realidade da comunidade, e por ser comum às crianças. Diferentes estratégias educacionais foram desenvolvidas para trabalhar o tema, entre elas a visita de campo ao manguezal e a construção de um painel coletivo. Conclui-se, ao final do estudo, que a prática pedagógica desenvolvida constitui-se como educação popular do campo voltada para a educação infantil, porque cumpre a finalidade conceitual de oferecer uma educação diferenciada para os sujeitos do campo, respeitando o modo de vida e cultura deles.
Social Sciences, Labor in politics. Political activity of the working class
Loren Neves de Miranda, Carla Denari Giuliani, Renata Sobreira Fernandes
et al.
A descentralização do atendimento em saúde mental para a Atenção Primária à Saúde (APS) e Centros de Apoio Psicossocial (CAPS) foi de extrema importância para garantir maior qualidade do cuidado. No entanto, o manejo dos casos de transtornos mentais na APS ainda é desafiador. Neste trabalho, relatamos a experiência desenvolvida por um grupo de estudantes, tutores e preceptores do Programa de Educação em Saúde pelo Trabalho (PET-Saúde) na construção de uma oficina de formação para o uso do Projeto Terapêutico Singular (PTS) para profissionais da saúde. A oficina foi desenvolvida em um município mineiro de porte médio, em março de 2023, e teve a participação de mais de 100 trabalhadores. Além de atividades teóricas, a oficina contou com atividades práticas, o que possibilitou que os participantes reconhecessem as dificuldades do processo de trabalho, trocassem experiências e propusessem soluções para aprimorar o atendimento em saúde mental no município.
Social Sciences, Labor in politics. Political activity of the working class
Online social platforms allow users to filter out content they do not like. According to selective exposure theory, people tend to view content they agree with more to get more self-assurance. This causes people to live in ideological filter bubbles. We report on a user study that encourages users to break the political filter bubble of their Twitter feed by reading more diverse viewpoints through social comparison. The user study is conducted using political-bias analyzing and Twitter-mirroring tools to compare the political slant of what a user reads and what other Twitter users read about a topic, and in general. The results show that social comparison can have a great impact on users' reading behavior by motivating them to read viewpoints from the opposing political party.
The assessment of bias within Large Language Models (LLMs) has emerged as a critical concern in the contemporary discourse surrounding Artificial Intelligence (AI) in the context of their potential impact on societal dynamics. Recognizing and considering political bias within LLM applications is especially important when closing in on the tipping point toward performative prediction. Then, being educated about potential effects and the societal behavior LLMs can drive at scale due to their interplay with human operators. In this way, the upcoming elections of the European Parliament will not remain unaffected by LLMs. We evaluate the political bias of the currently most popular open-source LLMs (instruct or assistant models) concerning political issues within the European Union (EU) from a German voter's perspective. To do so, we use the "Wahl-O-Mat," a voting advice application used in Germany. From the voting advice of the "Wahl-O-Mat" we quantize the degree of alignment of LLMs with German political parties. We show that larger models, such as Llama3-70B, tend to align more closely with left-leaning political parties, while smaller models often remain neutral, particularly when prompted in English. The central finding is that LLMs are similarly biased, with low variances in the alignment concerning a specific party. Our findings underline the importance of rigorously assessing and making bias transparent in LLMs to safeguard the integrity and trustworthiness of applications that employ the capabilities of performative prediction and the invisible hand of machine learning prediction and language generation.
Farhad Moghimifar, Yuan-Fang Li, Robert Thomson
et al.
Coalition negotiations are a cornerstone of parliamentary democracies, characterised by complex interactions and strategic communications among political parties. Despite its significance, the modelling of these negotiations has remained unexplored with the domain of Natural Language Processing (NLP), mostly due to lack of proper data. In this paper, we introduce coalition negotiations as a novel NLP task, and model it as a negotiation between large language model-based agents. We introduce a multilingual dataset, POLCA, comprising manifestos of European political parties and coalition agreements over a number of elections in these countries. This dataset addresses the challenge of the current scope limitations in political negotiation modelling by providing a diverse, real-world basis for simulation. Additionally, we propose a hierarchical Markov decision process designed to simulate the process of coalition negotiation between political parties and predict the outcomes. We evaluate the performance of state-of-the-art large language models (LLMs) as agents in handling coalition negotiations, offering insights into their capabilities and paving the way for future advancements in political modelling.
Patrick T. Brandt, Sultan Alsarra, Vito J. D`Orazio
et al.
Conflict scholars have used rule-based approaches to extract information about political violence from news reports and texts. Recent Natural Language Processing developments move beyond rigid rule-based approaches. We review our recent ConfliBERT language model (Hu et al. 2022) to process political and violence related texts. The model can be used to extract actor and action classifications from texts about political conflict. When fine-tuned, results show that ConfliBERT has superior performance in accuracy, precision and recall over other large language models (LLM) like Google's Gemma 2 (9B), Meta's Llama 3.1 (7B), and Alibaba's Qwen 2.5 (14B) within its relevant domains. It is also hundreds of times faster than these more generalist LLMs. These results are illustrated using texts from the BBC, re3d, and the Global Terrorism Dataset (GTD).
Daniela Andressa Ferreira Viana, Maxwell Barbosa de Santana
As Neurociências são campos de conhecimento que procuram compreender os diferentes processos mentais e as bases subjacentes do Sistema Nervoso. Atualmente, ganharam maior enfoque por auxiliar na compreensão e na potencialização dos mecanismos de aprendizagem em diferentes níveis acadêmicos. Nesse sentido, o indivíduo jovem, na faixa de escolarização do Ensino Médio, é um ator ainda vulnerável na capacidade de tomar decisões que podem ser decorrentes da falta de conhecimento dos fenômenos que as Neurociências permitem elucidar. Assim, este projeto buscou levar para os jovens do Ensino Médio de uma escola pública do município de Santarém/Pará o conhecimento teórico e prático acerca de aplicações e conceitos das Neurociências. Para conhecer os diversos temas desta área, em cada semana abordou-se uma temática diferente que relacionava a integração do funcionamento do cérebro e do corpo. A experiência de introdução de conceitos de funcionamento do par “cérebro-mente” para esses jovens tornou a disseminação das Neurociências uma forma efetiva para popularizar essa área da ciência e os motivou a aprenderem e compreenderem a realidade dela, uma vez que a temática mescla diversas áreas do conhecimento.
Social Sciences, Labor in politics. Political activity of the working class
Peace means order, and war brings disorder and chaos to any society. But order and disorder are not only observed in wars, in many systems they are the dominant property. Understanding order and disorder enables us to understand the structure of systems. Order and disorder are also part of the Lagrange Principle, and as statistics is valid in all systems, we may regard Lagrange statistics as a mathematical basis of system science. Two systems out of natural and social science are compared: materials of trillions of atoms and politics of millions of people. Lagrange statistics leads to three phases of homogeneous systems: in materials we have the states: solid, liquid, gas, depending on two Lagrange parameters, temperature T (the mean energy of atoms) and pressure p. In politics we have three states: autocratic, democratic, global, depending on two Lagrange parameters, standard of living T (the mean capital of people) and political pressure p. The three phases of each system are compared in the p-T phase diagram: Different phases of one system cannot coexist as nearest neighbors: Water will dissolve ice by exchange of atoms and heat. This leads to the present climate crisis. Democracies will dissolve autocracies by exchange of goods, ideas, and people like guest workers. This is the peaceful history of the EU and has led to the aggressive reaction of Russia in the GDR, Hungary, ČSR, and now in the Ukraine. At the end of war peaceful coexistence will not be possible between Russia and Ukraine. Only separation by a new Iron Curtain guaranteed by NATO can lead to a long-time armistice.
We present a survey to evaluate crypto-political, crypto-economic, and crypto-governance sentiment in people who are part of a blockchain ecosystem. Based on 3710 survey responses, we describe their beliefs, attitudes, and modes of participation in crypto and investigate how self-reported political affiliation and blockchain ecosystem affiliation are associated with these. We observed polarization in questions on perceptions of the distribution of economic power, personal attitudes towards crypto, normative beliefs about the distribution of power in governance, and external regulation of blockchain technologies. Differences in political self-identification correlated with opinions on economic fairness, gender equity, decision-making power and how to obtain favorable regulation, while blockchain affiliation correlated with opinions on governance and regulation of crypto and respondents' semantic conception of crypto and personal goals for their involvement. We also find that a theory-driven constructed political axis is supported by the data and investigate the possibility of other groupings of respondents or beliefs arising from the data.
Giordano Paoletti, Lorenzo Dall'Amico, Kyriaki Kalimeri
et al.
At the beginning of the COVID-19 pandemic, fears grew that making vaccination a political (instead of public health) issue may impact the efficacy of this life-saving intervention, spurring the spread of vaccine-hesitant content. In this study, we examine whether there is a relationship between the political interest of social media users and their exposure to vaccine-hesitant content on Twitter. We focus on 17 European countries using a multilingual, longitudinal dataset of tweets spanning the period before COVID, up to the vaccine roll-out. We find that, in most countries, users' endorsement of vaccine-hesitant content is the highest in the early months of the pandemic, around the time of greatest scientific uncertainty. Further, users who follow politicians from right-wing parties, and those associated with authoritarian or anti-EU stances are more likely to endorse vaccine-hesitant content, whereas those following left-wing politicians, more pro-EU or liberal parties, are less likely. Somewhat surprisingly, politicians did not play an outsized role in the vaccine debates of their countries, receiving a similar number of retweets as other similarly popular users. This systematic, multi-country, longitudinal investigation of the connection of politics with vaccine hesitancy has important implications for public health policy and communication.
Many democratic political parties hold primary elections, which nicely reflects their democratic nature and promote, among other things, the democratic value of inclusiveness. However, the methods currently used for holding such primary elections may not be the most suitable, especially if some form of proportional ranking is desired. In this paper, we compare different algorithmic methods for holding primaries (i.e., different aggregation methods for voters' ballots), by evaluating the degree of proportional ranking that is achieved by each of them using real-world data. In particular, we compare six different algorithms by analyzing real-world data from a recent primary election conducted by the Israeli Democratit party. Technically, we analyze unique voter data and evaluate the proportionality achieved by means of cluster analysis, aiming at pinpointing the representation that is granted to different voter groups under each of the algorithmic methods considered. Our finding suggest that, contrary to the most-prominent primaries algorithm used (i.e., Approval), other methods such as Sequential Proportional Approval or Phragmen can bring about better proportional ranking and thus may be better suited for primary elections in practice.
Alberto Luiz Pereira da Costa, Angélica Carvalho Lemos
Os doces artesanais integram a categoria de patrimônio imaterial para a cultura regional. O presente trabalho tem como objetivo contemplar ações em prol da memória cultural e valorização do artesanato regional dos doces de Peirópolis, Uberaba-MG. Frente às múltiplas expressões culturais presentes no estado, ressaltamos a típica gastronomia mineira do fazer artesanal dos doces. A metodologia e o procedimento foram desenvolvidos por meio da etnografia nos Círculos de Cultura promovidos por Paulo Freire e que possibilitaram identificar a percepção das doceiras e doceiros integrantes de uma Associação Comunitária acerca da difusão da tradição do fazer manual. Na depuração dos dados, percebemos que a troca intergeracional é fortemente presente no modo de aprendizagem, inclui-se desde as receitas, a colheita e a seleção das frutas, até ao ponto do doce. O trabalho aponta para caminhos frutíferos do fazer artesanal, almejando a preservação e a valorização do patrimônio imaterial na comunidade local e ações extensionistas do Círculo de Cultura.
Social Sciences, Labor in politics. Political activity of the working class
Fernanda Costa Silva, Isadora Oliveira Gondim, Sonia Maria Soares
Este relato de experiência tem como objetivo descrever as ações e metodologias adotadas pelo projeto de extensão universitária “Ação multiplicadora: uma proposta de inclusão social e acessibilidade”, anos 2018 e 2019. As principais metodologias adotadas para o desenvolvimento das ações do projeto foram: pesquisa bibliográfica, entrevistas, preparação de materiais didáticos, seminários, exposições, observação e análise fílmica. O projeto contribuiu para ampliar as discussões acerca da inclusão social e acessibilidade das pessoas com deficiência e conscientizar atores sobre a necessidade de propiciar ambientes inclusivos, de socialização e acessíveis. Concluiu-se que o fortalecimento de projetos dessa natureza promove a reflexão da comunidade universitária e auxilia o processo de inclusão social e acessibilidade das pessoas com deficiência nas Instituições Federais de Ensino Superior.
Social Sciences, Labor in politics. Political activity of the working class