Hasil untuk "Socialism. Communism. Anarchism"

I. D’Adamo, Massimo Gastaldi, N. Hariram et al.

This paper delves into the multifaceted concept of sustainability, covering its evolution, laws, principles, as well as the different domains and challenges related to achieving it in the modern world. Although capitalism, socialism, and communism have been utilized throughout history, their strengths and drawbacks have failed to address sustainable development comprehensively. Therefore, a holistic approach is necessary, which forms the basis for a new development model called sustainalism. This study proposes a new socio-economic theory of sustainalism that prioritizes quality of life, social equity, culture, world peace, social justice, and well-being. This paper outlines the six principles of sustainalism and identifies sustainalists as individuals who embrace these new concepts. This study also explores how to attain sustainalism in the modern world through a sustainable revolution, representing a step toward a sustainable era. In conclusion, this paper summarizes the key points and emphasizes the need for a new approach to sustainalism in the broader sense. The insights provided are valuable for further research on sustainalism and sustainability.

Kohei Saito

Facing global climate crisis, Karl Marx's ecological critique of capitalism more clearly demonstrates its importance than ever. This book explains why Marx's ecology had to be marginalized and even suppressed by Marxists after his death throughout the twentieth century. Marx's ecological critique of capitalism, however, revives in the Anthropocene against dominant productivism and monism. Investigating new materials published in the complete works of Marx and Engels (Marx-Engels-Gesamtausgabe), Saito offers a wholly novel idea of Marx's alternative to capitalism that should be adequately characterized as degrowth communism. This provocative interpretation of the late Marx sheds new lights on the recent debates on the relationship between society and nature and invites readers to envision a post-capitalist society without repeating the failure of the actually existing socialism of the twentieth century.

Corina Rodríguez Enríquez, Verónica Gago, Paula Varela

En 2024, en una co-edición entre el CEHTI y Ediciones IPS en Argentina, Bellaterra en Barcelona y Proyección en Chile, fue publicado por primera vez en castellano El marxismo y la opresión de las mujeres. Hacia una teoría unitaria de Lise Vogel. Este libro, cuya primera edición fue en 1983, sienta las bases de lo que hoy se conoce como Teoría de la Reproducción Social. En este dossier invitamos a Corina Rodríguez Enríquez, Verónica Gago y Paula Varela a reflexionar sobre el libro y sus aportes a los debates de los feminismos contemporáneos.

Jacob Blumenfeld, Santiago M. Roggerone

Entrevista a Hans-Peter Gruber

Vinod Khandkar, Kieron Ivy Turk, Ehsan Toreini et al.

Rapidly changing social norms and national, legal, and political conditions socially constrain people from discussing sensitive topics such as sexuality or religion. Such constrained, vulnerable minorities are often worried about inadvertent information disclosure and may be unsure about the extent to which their communications are being monitored in public or semi-public spaces like workplaces or cafes. Personal devices extend trust to the digital domain, making it desirable to have strictly private communication between trusted devices. Currently, messaging services like WhatsApp provide alternative means for exchanging sensitive private information, while personal safety apps such as Noonlight enable private signaling. However, these rely on third-party mechanisms for secure and private communication, which may not be accessible for justifiable reasons, such as insecure internet access or companion device connections. In these cases, it is challenging to achieve communication that is strictly private between two devices instead of user accounts without any dependency on third-party infrastructure. The goal of this paper is to support private communications by setting up a shared secret between two or more devices without sending any data on the network. We develop a method to create a shared secret between phones by shaking them together. Each device extracts the shared randomness from the shake, then conditions the randomness to 7.798 bits per byte of key material. This paper proposes three different applications of this generated shared secret: message obfuscation, trust delegation, and encrypted beacons. We have implemented the message obfuscation on Android as an independent app that can be used for private communication with trusted contacts. We also present research on the usability, design considerations, and further integration of these tools in mainstream services.

Noel Chacko, Hannah Vy Nguyen, Sophie Chen et al.

Many barriers exist when new members join a research community, including impostor syndrome. These barriers can be especially challenging for undergraduate students who are new to research. In our work, we explore how the use of social computing tools in the form of spontaneous online social networks (SOSNs) can be used in small research communities to improve sense of belonging, peripheral awareness, and feelings of togetherness within an existing CS research community. Inspired by SOSNs such as BeReal, we integrated a Wizard-of-Oz photo sharing bot into a computing research lab to foster community building among members. Through a small sample of lab members (N = 17) over the course of 2 weeks, we observed an increase in participants' sense of togetherness based on pre- and post-study surveys. Our surveys and semi-structured interviews revealed that this approach has the potential to increase awareness of peers' personal lives, increase feelings of community, and reduce feelings of disconnectedness.

George Odysseos

This article explores the complicated impact of Stalinism’s political crisis and anti‐imperialist politics on Cypriot communism in the 1950s. While the year 1956 has featured prominently in the historiography of western communist parties, the date hardly features in studies of Cypriot communism, nor in the history of the communist AKEL (Progressive Party of Working People). These latter studies have instead focused on AKEL’s stance towards the right‐wing EOKA (National Organisation of Cypriot Fighters) campaign against British colonialism (1955‐1959). Nevertheless, many of the issues that lent their weight to the significance of 1956 ‐ such as the process of de‐Stalinisation and question of party discipline, as well as the dilemmas opened up by the development of ‘national roads’ to socialism ‐ played an important role in Cypriot communism during the period. AKEL, then, offers a case of a party connected to the ‘global 1956’ that did not centre on that particular calendar date. The article focuses on two prominent figures who were expelled from the party as a result of AKEL’s intra‐party crisis of 1952; the young lawyer George Cacogiannis, and Evdoros Joannides, perhaps the party’s foremost propagandist resident in London. In discussing their writings and activities, the article reveals the global arena Cypriot communists operated within, highlighting the ways issues of international communism were adapted to Cypriot conditions and concerns, which revolved around effort to ‘internationalise’ the national issue. In doing so, it demonstrates that, for some parties, the resonance of the Suez Crisis outlasted the shock of the Soviet invasion of Hungary, and offered a framework for the ‘recontextualisation’ of the Soviet Union through the Non-Aligned Movement.

Peter J. Boettke, Rosolino A. Candela, Tegan Truitt

For over a generation, the collapse of communism in the Soviet Union and Eastern and Central Europe delegitimized the abolition of private property in the means of production and the practice of central planning as an effective way to achieve the ends of socialism. However, the aspiration of achieving the ends of socialism remains to this day. This Element provides a narrative of a century-long debate that was initiated by Ludwig von Mises in 1920. In so doing, it tells the history of the problem of economic calculation in the socialist commonwealth and its continuing relevance for developments in economics, political economy, and social philosophy.

Yuwei Chuai, Moritz Pilarski, Thomas Renault et al.

Community-based fact-checking is a promising approach to verify social media content and correct misleading posts at scale. Yet, causal evidence regarding its effectiveness in reducing the spread of misinformation on social media is missing. Here, we performed a large-scale empirical study to analyze whether community notes reduce the spread of misleading posts on X. Using a Difference-in-Differences design and repost time series data for N=237,677 (community fact-checked) cascades that had been reposted more than 431 million times, we found that exposing users to community notes reduced the spread of misleading posts by, on average, 62.0%. Furthermore, community notes increased the odds that users delete their misleading posts by 103.4%. However, our findings also suggest that community notes might be too slow to intervene in the early (and most viral) stage of the diffusion. Our work offers important implications to enhance the effectiveness of community-based fact-checking approaches on social media.

Scott Timcke

n/a

Seth Karten, Siva Kailas, Huao Li et al.

Explicit communication among humans is key to coordinating and learning. Social learning, which uses cues from experts, can greatly benefit from the usage of explicit communication to align heterogeneous policies, reduce sample complexity, and solve partially observable tasks. Emergent communication, a type of explicit communication, studies the creation of an artificial language to encode a high task-utility message directly from data. However, in most cases, emergent communication sends insufficiently compressed messages with little or null information, which also may not be understandable to a third-party listener. This paper proposes an unsupervised method based on the information bottleneck to capture both referential complexity and task-specific utility to adequately explore sparse social communication scenarios in multi-agent reinforcement learning (MARL). We show that our model is able to i) develop a natural-language-inspired lexicon of messages that is independently composed of a set of emergent concepts, which span the observations and intents with minimal bits, ii) develop communication to align the action policies of heterogeneous agents with dissimilar feature models, and iii) learn a communication policy from watching an expert's action policy, which we term `social shadowing'.

Quindel Jones, Andrés R. Vindas Meléndez, Ariana Mendible et al.

Data science for social justice (DS4SJ) is data-scientific work that supports the liberation of oppressed and marginalized people. By nature, this work lies at the intersection of technical scholarship and activist practice. We discuss this growing efforts in DS4SJ within the broad mathematics community. We begin by defining terms and offering a series of guiding principles for engaging in critical data science work, providing examples of how these principles play out in practice. We then highlight the roles that DS4SJ can play in the scholarship and pedagogy of practicing mathematicians. We focus in particular on the engagement of early-career mathematicians in DS4SJ, which we illustrate through a series of four personal vignettes. While the primary aim of DS4SJ is to achieve impact for marginalized communities, we also argue that engagement with DS4SJ can benefit the entire mathematical ecosystem, including researchers, instructors, students, departments, institutes, and professional societies. We close with reflections on how these various actors can support ongoing efforts in data science for social justice.

Eduardo Cabllero-Espinosa, Jeffrey C. Carver, Kimberly Stowers

Context: Social debt describes the accumulation of unforeseen project costs (or potential costs) from sub-optimal software development processes. Community smells are sociotechnical anti-patterns and one source of social debt that impact software teams, development processes, outcomes, and organizations. Objective: To provide an overview of community smells based on published literature, and describe future research. Method: We conducted a systematic literature review (SLR) to identify properties, understand origins and evolution, and describe the emergence of community smells. This SLR explains the impact of community smells on teamwork and team performance. Results: We include 25 studies. Social debt describes the impacts of poor socio-technical decisions on work environments, people, software products, and society. For each of the 30 identified community smells, we provide a description, management approaches, organizational strategies, and mitigation effectiveness. We identify five groups of management approaches: organizational strategies, frameworks, models, tools, and guidelines. We describe 11 properties of community smells. We develop the Community Smell Stages Framework to concisely describe the origin and evolution of community smells. We describe the causes and effects for each community smell. We identify and describe 8 types of causes and 11 types of effects for community smells. Finally, we provide 8 Sankey diagrams that offer insights into threats the community smells pose to teamwork factors and team performance. Conclusion: Community smells explain the influence work conditions have on software developers. The literature is scarce and focuses on a small number of community smells. Thus, community smells still need more research. This review organizes the state of the art about community smells and provides motivation for future research along with educational material.

Joon Sung Park, Joseph Seering, Michael S. Bernstein

With increasing attention to online anti-social behaviors such as personal attacks and bigotry, it is critical to have an accurate accounting of how widespread anti-social behaviors are. In this paper, we empirically measure the prevalence of anti-social behavior in one of the world's most popular online community platforms. We operationalize this goal as measuring the proportion of unmoderated comments in the 97 most popular communities on Reddit that violate eight widely accepted platform norms. To achieve this goal, we contribute a human-AI pipeline for identifying these violations and a bootstrap sampling method to quantify measurement uncertainty. We find that 6.25% (95% Confidence Interval [5.36%, 7.13%]) of all comments in 2016, and 4.28% (95% CI [2.50%, 6.26%]) in 2020-2021, are violations of these norms. Most anti-social behaviors remain unmoderated: moderators only removed one in twenty violating comments in 2016, and one in ten violating comments in 2020. Personal attacks were the most prevalent category of norm violation; pornography and bigotry were the most likely to be moderated, while politically inflammatory comments and misogyny/vulgarity were the least likely to be moderated. This paper offers a method and set of empirical results for tracking these phenomena as both the social practices (e.g., moderation) and technical practices (e.g., design) evolve.

Ezequiel Murmis, Mercedes López Cantera

In memoriam

Xixi Li, Yun Bai, Yanfei Kang

One of the most significant differences of M5 over previous forecasting competitions is that it was held on Kaggle, an online platform of data scientists and machine learning practitioners. Kaggle provides a gathering place, or virtual community, for web users who are interested in the M5 competition. Users can share code, models, features, loss functions, etc. through online notebooks and discussion forums. This paper aims to study the social influence of virtual community on user behaviors in the M5 competition. We first research the content of the M5 virtual community by topic modeling and trend analysis. Further, we perform social media analysis to identify the potential relationship network of the virtual community. We study the roles and characteristics of some key participants that promote the diffusion of information within the M5 virtual community. Overall, this study provides in-depth insights into the mechanism of the virtual community's influence on the participants and has potential implications for future online competitions.

Shruti Phadke, Mattia Samory, Tanushree Mitra

Widespread conspiracy theories, like those motivating anti-vaccination attitudes or climate change denial, propel collective action and bear society-wide consequences. Yet, empirical research has largely studied conspiracy theory adoption as an individual pursuit, rather than as a socially mediated process. What makes users join communities endorsing and spreading conspiracy theories? We leverage longitudinal data from 56 conspiracy communities on Reddit to compare individual and social factors determining which users join the communities. Using a quasi-experimental approach, we first identify 30K future conspiracists-(FC) and 30K matched non-conspiracists-(NC). We then provide empirical evidence of importance of social factors across six dimensions relative to the individual factors by analyzing 6 million Reddit comments and posts. Specifically in social factors, we find that dyadic interactions with members of the conspiracy communities and marginalization outside of the conspiracy communities, are the most important social precursors to conspiracy joining-even outperforming individual factor baselines. Our results offer quantitative backing to understand social processes and echo chamber effects in conspiratorial engagement, with important implications for democratic institutions and online communities.

Chonggang Song, Qian Lin, Guohui Ling et al.

Relationships in online social networks often imply social connections in the real world. An accurate understanding of relationship types benefits many applications, e.g. social advertising and recommendation. Some recent attempts have been proposed to classify user relationships into predefined types with the help of pre-labeled relationships or abundant interaction features on relationships. Unfortunately, both relationship feature data and label data are very sparse in real social platforms like WeChat, rendering existing methods inapplicable. In this paper, we present an in-depth analysis of WeChat relationships to identify the major challenges for the relationship classification task. To tackle the challenges, we propose a Local Community-based Edge Classification (LoCEC) framework that classifies user relationships in a social network into real-world social connection types. LoCEC enforces a three-phase processing, namely local community detection, community classification and relationship classification, to address the sparsity issue of relationship features and relationship labels. Moreover, LoCEC is designed to handle large-scale networks by allowing parallel and distributed processing. We conduct extensive experiments on the real-world WeChat network with hundreds of billions of edges to validate the effectiveness and efficiency of LoCEC.