Outline of a Theory of Practice is recognized as a major theoretical text on the foundations of anthropology and sociology. Pierre Bourdieu, a distinguished French anthropologist, develops a theory of practice which is simultaneously a critique of the methods and postures of social science and a general account of how human action should be understood. With his central concept of the habitus, the principle which negotiates between objective structures and practices, Bourdieu is able to transcend the dichotomies which have shaped theoretical thinking about the social world. The author draws on his fieldwork in Kabylia (Algeria) to illustrate his theoretical propositions. With detailed study of matrimonial strategies and the role of rite and myth, he analyses the dialectical process of the 'incorporation of structures' and the objectification of habitus, whereby social formations tend to reproduce themselves. A rigorous consistent materialist approach lays the foundations for a theory of symbolic capital and, through analysis of the different modes of domination, a theory of symbolic power.
Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research. This is especially important in the task of molecular graph generation, whose goal is to discover novel molecules with desired properties such as drug-likeness and synthetic accessibility, while obeying physical laws such as chemical valency. However, designing models to find molecules that optimize desired properties while incorporating highly complex and non-differentiable rules remains to be a challenging task. Here we propose Graph Convolutional Policy Network (GCPN), a general graph convolutional network based model for goal-directed graph generation through reinforcement learning. The model is trained to optimize domain-specific rewards and adversarial loss through policy gradient, and acts in an environment that incorporates domain-specific rules. Experimental results show that GCPN can achieve 61% improvement on chemical property optimization over state-of-the-art baselines while resembling known molecules, and achieve 184% improvement on the constrained property optimization task.
Network Notation Networks are often characterized by clusters of constituents that interact more closely with each other and have more connections to one another than they do with the rest of the components of the network. However, systematically identifying and studying such community structure in complicated networks is not easy, especially when the network interactions change over time or contain multiple types of connections, as seen in many biological regulatory networks or social networks. Mucha et al. (p. 876) developed a mathematical method to allow detection of communities that may be critical functional units of such networks. Application to real-world tasks—like making sense of the voting record in the U.S. Senate—demonstrated the promise of the method. A general mathematical method used to identify closely interacting groups can explain the behavior of complicated networks. Network science is an interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly connected groups of nodes known as communities. We developed a generalized framework of network quality functions that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that connect each node in one network slice to itself in other slices. This framework allows studies of community structure in a general setting encompassing networks that evolve over time, have multiple types of links (multiplexity), and have multiple scales.
Alexandros Efstratiou, Giuseppe Russo, Luca Luceri
Online discussions of science involve complex interactions among experts, news media, and social media users as they interpret and disseminate scientific findings. While prior work has examined these actors in isolation, their interplay in shaping science communication remains poorly understood. Using the COVID-19 pandemic as a case study, we analyze 1.24M tweets and 211k news articles that reference pandemic-related scientific papers. We find that the most influential Twitter accounts in this discourse are predominantly individuals with medical or research credentials. However, we also identify a coordinated network that disproportionately amplifies a small set of prominent credentialed experts who advance contrarian, anti-consensus positions on vaccines, lockdowns, and related topics. The papers promoted by these influential actors substantially overlap with those covered by news media, but with key differences: pro-consensus experts primarily engage with studies featured by mainstream and medical outlets, whereas contrarian experts align more closely with papers promoted by low-quality, pseudoscientific, or conspiratorial sources. Notably, news outlets tend to report on scientific studies after they have been highlighted by social media superspreaders. Together, these findings reveal multi-level pathways of information flow and coordinated amplification structures that shape science communication across social media and news, offering new insights into the dynamics of the broader information ecosystem.
Through my work as an artist, educator, and activist, I aim to promote the interconnectedness of arts, management, creativity, curating, research, and technology by developing courses for art academies, art students, and interdisciplinary students at universities. I believe that future university and art academy education will increasingly integrate interdisciplinarity, combining fields such as management, creativity, technology (including artificial intelligence), curating, and other disciplines. This approach aligns with the interdisciplinary nature of professional life after graduation, whether one pursues a career as an artist or as a director of an institution. I reference both the art academy and university environments due to my experience as a lecturer in both settings. The article explores the relationship between authentic leadership and creativity within the context of university pedagogy, utilizing a reflexive research method based on a case study of creativity education. This case study focuses solely on the university environment, specifically on teaching creativity to interdisciplinary groups of students. My experience as a lecturer contains teaching artists, performers, and interdisciplinary students at both the Bachelor of Arts and Master of Arts levels. Data includes my notes as a facilitator-lecturer, as well as the creative output and feedback from the interdisciplinary student groups. This data is analyzed using qualitative methods. The case study, derived from a Digital Creativity course, is examined speculatively to assess how the skills acquired may impact students post-graduation. As I was not in contact with students after the course ended, observations are limited to the processes observed during the course itself. The research question addressed is: how can digital learning of authentic leadership and creativity be facilitated within university pedagogy to create a safe and empathetic learning environment for students? Leadership is discussed from the perspective of authentic leadership and its relationship to creativity. Digital literacy is framed as critical thinking and responsibility in the context of collaboration with non-human entities, such as artificial intelligence. This study is relevant for university and art academy professionals working in cross-disciplinary contexts related to authentic leadership and creativity.
AbstractDespite a recent line of research highlighting trust as an important determinant of residents’ happiness in Western countries, empirical evidence regarding the strength of these linkages in the developing world needs to be more comprehensive and conclusive. This paper contributes to this literature by performing a deeper examination into the trust-based explanation of happiness and, particularly, exploring the mediating role of social capital in rural China, where rapid economic growth coexists with gradual and fundamental social changes. Using data from the nationally representative cross-sectional Chinese General Social Survey (CGSS) in 2012, 2013, and 2015, we indicate that trust positively affects the happiness of rural residents and social capital. Our findings support the role of social capital in mediating happiness prediction. Furthermore, additional tests suggest heterogeneous social capital for different ages and degrees of regional market competition. Specifically, the mediating effect of social capital on rural residents over 30 years old (inclusive) and living in high-market competition areas is significant. These findings provide a valuable direction for the government that producing an environment that enhances social networks and facilitates the exchange of social support holds promise for improving the life satisfaction of the rural Chinese population. Trust can significantly improve rural residents’ happiness through increasing social capital. The government should effectively manage the rural social trust crisis and help rural residents build social capital in multiple ways, improving the life satisfaction of rural residents.
The term tipping point has experienced explosive popularity across multiple disciplines over the last decade. Research on social-ecological systems (SES) has contributed to the growth and diversity of the term’s use. The diverse uses of the term obscure potential differences between tipping behavior in natural and social systems, and issues of causality across natural and social system components in SES. This paper aims to create the foundation for a discussion within the SES research community about the appropriate use of the term tipping point, especially the relatively novel term ‘social tipping point.’ We review existing literature on tipping points and similar concepts (e.g. regime shifts, critical transitions) across all spheres of science published between 1960 and 2016 with a special focus on a recent and still small body of work on social tipping points. We combine quantitative and qualitative analyses in a bibliometric approach, rooted in an expert elicitation process. We find that the term tipping point became popular after the year 2000—long after the terms regime shift and critical transition—across all spheres of science. We identify 23 distinct features of tipping point definitions and their prevalence across disciplines, but find no clear taxonomy of discipline-specific definitions. Building on the most frequently used features, we propose definitions for tipping points in general and social tipping points in SES in particular.