Hasil untuk "Socialism. Communism. Anarchism"

Matthew O. Jackson

I discuss economic and social sources of inequality and elaborate on the role of social networks in inequality, economic immobility, and economic inefficiencies. The lens of social networks clarifies how the entanglement of people's information, opportunities, and behaviors with those of their friends and family leads to persistent differences across communities, resulting in inequality in education, employment, income, health, and wealth. The key role of homophily in separating groups within the network is highlighted. A network perspective's policy implications differ substantially from a narrower economic perspective that ignores social structure. I discuss the importance of ``policy cocktails'' that include aspects that are aimed at both the economic and social forces driving inequality.

Leena Mathur, Marian Qian, Paul Pu Liang et al.

Social reasoning abilities are crucial for AI systems to effectively interpret and respond to multimodal human communication and interaction within social contexts. We introduce SOCIAL GENOME, the first benchmark for fine-grained, grounded social reasoning abilities of multimodal models. SOCIAL GENOME contains 272 videos of interactions and 1,486 human-annotated reasoning traces related to inferences about these interactions. These traces contain 5,777 reasoning steps that reference evidence from visual cues, verbal cues, vocal cues, and external knowledge (contextual knowledge external to videos). SOCIAL GENOME is also the first modeling challenge to study external knowledge in social reasoning. SOCIAL GENOME computes metrics to holistically evaluate semantic and structural qualities of model-generated social reasoning traces. We demonstrate the utility of SOCIAL GENOME through experiments with state-of-the-art models, identifying performance gaps and opportunities for future research to improve the grounded social reasoning abilities of multimodal models.

Jumana Almahmoud, Marc Facciotti, Michele Igo et al.

Social annotation platforms enable student engagement by integrating discussions directly into course materials. However, in large online courses, the sheer volume of comments can overwhelm students and impede learning. This paper investigates community-based design interventions on a social annotation platform (NB) to address this challenge and foster more meaningful online educational discussions. By examining student preferences and reactions to different curation strategies, this research aims to optimize the utility of social annotations in educational contexts. A key emphasis is placed on how the visibility of comments shapes group interactions, guides conversational flows, and enriches learning experiences. The study combined iterative design and development with two large-scale experiments to create and refine comment curation strategies, involving thousands of students. The study introduced specific features of the platform, such as targeted comment visibility controls, which demonstrably improved peer interactions and reduced discussion overload. These findings inform the design of next-generation social annotation systems and highlight opportunities to integrate Large Language Models (LLMs) for key activities like summarizing annotations, improving clarity in student writing, and assisting instructors with efficient comment curation.

Emil Grosfilley, Yujie Mu, Dap De Bruijckere

Social relations have been shown to impact individual and group success in farm animal populations. Fundamental to addressing these relationships is an understanding of the social network structure resulting from the co-habitation and co-movement of relationships between individuals in a group. Here, we investigate the social network of a group of around 210 lactating dairy cows on a dutch farm during a 14 days period. A positioning system called \emph{Cowview} collected positional data for the whole period. We make the assumption that spatial proximity can be used as a proxy for social interaction. The data is processed to get adjacency matrices. Then social networks are identified based on these matrices. Community detection techniques are applied to the networks. We measure metrics of different dimensions to test community structure, centralization, and similarity of network structure over time. Our study show that there is no evidence that cows are subdivided into stable social communities when looking at interaction in the whole barn. We, however, notice relatively clear communities when dividing the barn into areas with different activities. The social network is characterized by significant centralization, low connectivity, and a hierarchy.

Arianna Cortesi, Claudia Mignone, Alan Alves Brito et al.

The way we look at the sky is connected to the cosmological paradigm embraced by the society we live in. On the other hand, several astronomical concepts reinforce the idea of a common humanity. Yet, scientific outreach is frequenty reaching out only to a specific part of the world population, often excluding people living in extreme social vulnerability, victims of violence and prejudice, fighting for their lives and for the right of living according to their traditions. We present two outreach projects, developed in Brazil, funded by the Office of Astronomy for Development (OAD) of the International Astronomical Union (IAU), i.e. 'Under Other Skies' and 'OruMbya', which tackle the importance of ethno-astronomy, and the collaboration with leaders and cultural agents of marginalised communities. We also describe an educational project born in the favela of Cantagalo Pavão Pavaãzinho (PPG), in Rio de Janeiro, during the COVID19 pandemic, which started a collaboration with local educators and artists to offer classes of astronomy and English language to children in the favela

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Ron Buckmire, Joseph E. Hibdon,, Drew Lewis et al.

We present and discuss a curated selection of recent literature related to the application of quantitative techniques, tools, and topics from mathematics and data science that have been used to analyze the mathematical sciences community. We engage in this project with a focus on including research that highlights, documents, or quantifies (in)equities that exist in the mathematical sciences, specifically, and STEM (science, technology, engineering, and mathematics) more broadly. We seek to enhance social justice in the mathematics and data science communities by providing numerous examples of the ways in which the mathematical sciences fails to meet standards of equity, equal opportunity and inclusion. We introduce the term ``mathematics of Mathematics" for this project, explicitly building upon the growing, interdisciplinary field known as ``Science of Science" to interrogate, investigate, and identify the nature of the mathematical sciences itself. We aim to promote, provide, and posit sources of productive collaborations and we invite interested researchers to contribute to this developing body of work.

Ole Martin Rønning

Kristina Lerman

The social web has linked people on a global scale, transforming how we communicate and interact. The massive interconnectedness has created new vulnerabilities in the form of social manipulation and misinformation. As the social web matures, we are entering a new phase, where people share their private feelings and emotions. This so-called social emotional web creates new opportunities for human flourishing, but also exposes new vulnerabilities. To reap the benefits of the social emotional web, and reduce potential harms, we must anticipate how it will evolve and create policies that minimize risks.

Agustín Lucas Prestifilippo

Reseña de Santiago M. Roggerone, Tras las huellas del marxismo occidental, Buenos Aires, Ediciones IPS, 2022, 140 pgs.

Nicolas E. Diaz Ferreyra, Tobias Hecking, Esma Aïmeur et al.

Access-Control Lists (ACLs) (a.k.a. friend lists) are one of the most important privacy features of Online Social Networks (OSNs) as they allow users to restrict the audience of their publications. Nevertheless, creating and maintaining custom ACLs can introduce a high cognitive burden on average OSNs users since it normally requires assessing the trustworthiness of a large number of contacts. In principle, community detection algorithms can be leveraged to support the generation of ACLs by mapping a set of examples (i.e. contacts labelled as untrusted) to the emerging communities inside the user's ego-network. However, unlike users' access-control preferences, traditional community-detection algorithms do not take the homophily characteristics of such communities into account (i.e. attributes shared among members). Consequently, this strategy may lead to inaccurate ACL configurations and privacy breaches under certain homophily scenarios. This work investigates the use of community-detection algorithms for the automatic generation of ACLs in OSNs. Particularly, it analyses the performance of the aforementioned approach under different homophily conditions through a simulation model. Furthermore, since private information may reach the scope of untrusted recipients through the re-sharing affordances of OSNs, information diffusion processes are also modelled and taken explicitly into account. Altogether, the removal of gatekeeper nodes is further explored as a strategy to counteract unwanted data dissemination.

Sahraoui Dhelim, Huansheng Ning, Fadi Farha et al.

With the recent advances of the Internet of Things, and the increasing accessibility of ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as relationship explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as Artificial Social Intelligence (ASI) that has the potential to tackle the social relationship explosion problem. This paper discusses the role of IoT in social relationships detection and management, the problem of social relationships explosion in IoT and reviews the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques.

Jianzheng Wang, Guoqiang Hu

In this paper, we consider a future electricity market consisting of aggregated energy prosumers, who are equipped with local wind power plants (WPPs) to support (part of) their energy demands and can also trade energy with day-ahead market (DAM) and energy balancing market (EBM). In addition, an energy aggregator (EA) is established, who can provide the trading gateways between prosumers and the markets. The EA is responsible for making pricing strategies on the prosumers to influence their trading behaviours such that the social benefit of the prosumer community is improved. Specifically, two price packages are provided by the EA: wholesale price (WP) package and lump-sum (LS) package, which can be flexibly selected by prosumers based on their own preferences. Analytical energy-trading strategies will be derived for WP prosumers and LS prosumers based on non-cooperative games and Nash resource allocation strategies, respectively. In this work, a social cost optimization problem will be formulated for the EA, where the detailed WP/LS selection plans are unknown in advance. Consequently, a stochastic Stackelberg game between prosumers and the EA is formulated, and a two-level stochastic convex programming algorithm is proposed to minimize the expectation of the social cost. The performance of the proposed algorithm is demonstrated with a two-settlement based market model in the simulation.

Santiago M. Roggerone

Elías J. Palti es uno de los mayores referentes de la historia intelectual en América Latina. Es Doctor en Historia por la Universidad de California en Berkeley y se desempeña como Investigador Principal del CONICET con sede de trabajo en el Centro de Historia Intelectual, dependencia de la Universidad Nacional de Quilmes de la que actualmente es director. Ejerce la docencia, asimismo, tanto en esta última Universidad como en la Universidad de Buenos Aires. Entre sus numerosas publicaciones, destacan los libros Giro lingüístico e historia intelectual (1998), La nación como problema (2003), El tiempo de la política (2007), ¿Las ideas fuera de lugar? (2015) y Una arqueología de lo político (2018).

Jason Garner, José Benclowicz

Este artículo examina la conflictiva relación establecida entre los anarquistas españoles y el movimiento cooperativo desde la fundación de la Confederación Nacional del Trabajo hasta la Guerra Civil, centrándose en el caso de Cataluña. Teniendo en cuenta los antecedentes de esa relación, se analizan las tensiones presentes en los discursos de los padres fundadores del anarquismo, los posicionamientos disímiles que habilitaron y los sentidos que fueron asumiendo en función de la evolución del contexto histórico, pasando de un fuerte rechazo inicial a un acercamiento signado primero por el fracaso del movimiento huelguístico de posguerra y luego por el curso que fue asumiendo la Guerra Civil.

Cristina Viano

¿Cómo se ha producido el enlace entre izquierdas y feminismos en un territorio distinto al de los partidos de cuño marxista leninista? Este artículo se interroga sobre la experiencia desplegada por el Espacio de Mujeres que nació en la Anibal Verón y se prolongó y multiplicó posteriormente en el Frente Popular Darío Santillán (FPDS) para analizar cómo y de que modos se fueron abriendo paso desobediencias que aunaron un  cuestionamiento al orden de género patriarcal y al orden capitalista al interior de un movimiento  socioterritorial multisectorial habitado principalmente por desocupados.

Corina Turșie

Within the ECoC programme, it has been argued that the European dimension is most visible when the candidates reß ect their own history as a part of European history, particularly when hinting at their involvement with the major ideologies of twentieth century, such as National Socialism or Communism. ECoC is about cities re-inventing their identities, re-narrating their history in a European context. But how should ex-communist cities deal with their unwanted past and narrate it in order to Þ t into the European dimension of the project? The focus of this investigation is on three ex-ECOCs from ex-Communist Europe, chosen for several reasons: geographical position (Central, Eastern/Northern European countries, ex-communist past, new membership of the European Union (since 2004), the year of holding the title (the two ECoC selection criteria exist since 2010). Using qualitative content analysis on a set of documents (application books, ofÞ cial web pages and ex-post evaluations) the study will offer an analysis of cities’ politics of memory and urban regeneration strategies.

Michael Saman

Abstract W. E. B. Du Bois’s engagement with the thought of Karl Marx forms an important aspect of his intellectual biography, yet its contours crystallize explicitly only late in his written work, and its development prior to the 1930s remains insufficiently understood. In order to bring to light the mix of criticisms, reservations, ideals, and inspirations that shape this reception, this article explores its trajectory as exhaustively as the available documentation permits, beginning from Du Bois’s early training in economics as a university student, continuing through his increasing attention to socialism in the early 1900s and his embrace of Soviet communism in the 1920s, and culminating in the 1930s in his teaching of Marx at Atlanta University and the overtly Marxian positions he adopts in Black Reconstruction (1935).

Amila Silva, Pei-Chi Lo, Ee-Peng Lim

Personal values have significant influence on individuals' behaviors, preferences, and decision making. It is therefore not a surprise that personal values of a person could influence his or her social media content and activities. Instead of getting users to complete personal value questionnaire, researchers have looked into a non-intrusive and highly scalable approach to predict personal values using user-generated social media data. Nevertheless, geographical differences in word usage and profile information are issues to be addressed when designing such prediction models. In this work, we focus on analyzing Singapore users' personal values, and developing effective models to predict their personal values using their Facebook data. These models leverage on word categories in Linguistic Inquiry and Word Count (LIWC) and correlations among personal values. The LIWC word categories are adapted to non-English word use in Singapore. We incorporate the correlations among personal values into our proposed Stack Model consisting of a task-specific layer of base models and a cross-stitch layer model. Through experiments, we show that our proposed model predicts personal values with considerable improvement of accuracy over the previous works. Moreover, we use the stack model to predict the personal values of a large community of Twitter users using their public tweet content and empirically derive several interesting findings about their online behavior consistent with earlier findings in the social science and social media literature.

Vahid Moraveji Hashemi

The confluence of technological and societal advances is changing the nature of global terrorism. For example, engagement with Web, social media, and smart devices has the potential to affect the mental behavior of the individuals and influence extremist and criminal behaviors such as Radicalization. In this context, social data analytics (i.e., the discovery, interpretation, and communication of meaningful patterns in social data) and influence maximization (i.e., the problem of finding a small subset of nodes in a social network which can maximize the propagation of influence) has the potential to become a vital asset to explore the factors involved in influencing people to participate in extremist activities. To address this challenge, we study and analyze the recent work done in influence maximization and social data analytics from effectiveness, efficiency and scalability viewpoints. We introduce a social data analytics pipeline, namely iRadical, to enable analysts engage with social data to explore the potential for online radicalization. In iRadical, we present algorithms to analyse the social data as well as the user activity patterns to learn how influence flows in social networks. We implement iRadical as an extensible architecture that is publicly available on GitHub and present the evaluation results.