This theoretical article contributes with a theoretical model to explore and understand the role of cultural and symbolic drivers behind (un)sustainable patterns and volumes of consumption. More specifically, it addresses the symbolic drivers of consumption in relation to both embracing and resisting consumerism. Classic concepts and themes in the sociology of consumption such as identity, conspicuous consumption, consumerism, status, distinction, rituals, and social comparison are addressed. Through such concepts, the article addresses the contemporary problem of unsustainable mass consumption; that is, the late modern “consumerist” tendency to consume more/faster/larger/fancier, which results in constantly higher climate/ecological footprints. The article also addresses counterefforts to those tendencies; that is, efforts among people/movements/communities to resist consumerism and adopt lifestyles characterized by sufficiency. How are symbolic drivers involved in these opposite tendencies? What are the implications? To address such questions, the paper reviews recent scholarly discussions around how symbolic drivers connect with consumerism and consumerist critique.
Our contribution explores the media narrative of the war on Gaza, focusing on the role this has played in the representation of the victims on both sides of the conflict. To this end, using a critical discourse analysis approach, we’ll analyze Il Corriere della Sera’s media coverage of the first 15 days of the conflict, focusing on articles published in the national edition of the newspaper. In this way, we’ll show how the Corriere’s narrative has shaped an asymmetric representation of the victims, promoting identification and empathy with Israeli civilians and relegating the subjective and individual dimension of the Gazawi to the background, transforming their everyday experience into mere “bare life”.
Moral ambivalence has long characterized consumer practices and discourses, becoming more pronounced as sustainability gains relevance. This paper argues that sustainable consumption is not normatively neutral: it participates in the production of social distinction through competing claims to moral legitimacy. It develops the concept of conspicuous sustainability to capture how sustainability operates as a visible marker of moral worth and social positioning in contemporary consumer culture. Rather than treating visibility as a superficial dimension of display, the paper conceptualizes it as a mechanism through which ecological legitimacy is produced, distributed, and contested. Considering the tensions animating conspicuous sustainable consumption, it shows how moralized visibility structures the recognition of environmental subjects, generating asymmetries in ecological valuation whereby some practices are publicly legitimized while others remain invisible or devalued despite comparable environmental impact. The paper further argues that such dynamics may generate normative resistance when dominant sustainability narratives are experienced as exclusionary and ultimately extractive. Developed as part of the Symposium “Conspicuous Sustainability: Consumption, Climate Change and Morality”, which it introduces, the paper highlights sustainability as an arena of moral and political valuation in which legitimacy, inequality, and recognition are continuously negotiated.
Class critiques of ethical consumption function as conventional wisdom, and green goods are often dismissed as luxuries for the affluent. While these critiques have merit, they can obscure the paradoxes at the heart of sustainable consumption, where moral aspiration is entangled with material constraints. To examine these dynamics, we use the case of “happy meat”, a shorthand for ethically branded animal products marketed as humane, local, or sustainable. Drawing on a broad Canadian multi-site study, we develop two analytic paradoxes that likely extend beyond food. The moral privilege paradox captures how ethical consumption organizes care through forms of distinction that also reproduce exclusion. The narrative scale paradox highlights how reassuring cultural narratives of happy meat make ethical consumption feel meaningful and achievable in ways that can also narrow recognition of the structural limits of small-scale solutions. Together, these paradoxes show how sustainable consumption can generate meaningful ethical actions while also reproducing the exclusions and limitations that undermine its broader promise. Seeing sustainable consumption through this lens helps move beyond the familiar cul-de-sac where consumption is cast either as empty performance or as a hopeful path to sustainability. Instead, it reveals sustainable consumption as a project shaped by enduring contradictions and illuminates how people navigate competing moral, material, and cultural demands within contemporary food systems.
This article offers a retrospective sociological reassessment of the COVID-19 pandemic as a case of dramatic social change (DSC), examining which transformations proved enduring, which were reversed, and how the crisis exposed the theoretical and methodological limits of sociological analysis. Drawing on the DSC framework, the paper analyses the pandemic as a large-scale disruptive event affecting social structures, institutional trust, digitalization, and social inequalities. Rather than treating the pandemic as a uniform rupture, the article emphasizes the temporal, uneven, and context-dependent character of crisis-induced transformations, showing that processes of persistence, adaptation, and reconfiguration unfolded differently across social domains. In this sense, the pandemic is interpreted not only as a catalyst for change but also as a lens through which existing structural tensions and inequalities became more visible. The article further contributes by extending the DSC framework beyond its predominantly psychological applications, demonstrating its relevance for analyzing institutional dynamics, collective identities, and crisis governance from a sociological perspective. At the methodological level, it engages with critiques of linear explanatory models, arguing that global crises such as COVID-19 are better understood as non-linear, co-evolving processes characterized by interdependencies, feedback loops, and temporal variability. By situating the pandemic within a broader context of polycrisis, the paper highlights the need for multi-level, flexible, and reflexive sociological approaches capable of capturing complex and unstable forms of social change.
The meaning of interpretivism is shifting. Its key characteristics — a focus on multiple meanings, the symbolic nature of interaction, the ongoing and performative making of social and material worlds, and the use qualitative methodologies — are being problematized by digitization generally and AI specifically. At the very least, digitization and, thus, quantification, underpins a significant portion of contemporary meaning-making processes and interactions. To understand social life, interpretivism must account for this quantification and, often in turn, needs to use at least some of these tools of quantification for the purpose of achieving the hermeneutic goal of understanding. In the process, the meaning of interpretivism is changing in ways that are often subliminal and thus not clearly accounted for. For example, computational grounded theory asserts that the goal of grounded theory has always been to “measure” meaning. Has it? Having been trained in grounded theory, this characterization does not resonate with me. While measurement may aid interpretation, the goals of these two activities do vary, and we should be clear about their differences. I will argue that in this era it is crucial that we be careful about how “interpretive” is used because it remains indispensable and is different from measurement. My goal is not to police boundaries, but rather to emphasize the specificity of what qualitative research means. This is necessary to understand how AI is changing research as well as how qualitative research can reconfigure the discussions regarding the meanings of AI.
The aspiration for a social physics — a science that would reveal universal laws of social life analogous to those governing the natural world—has haunted the social sciences since Comte. Each new wave of quantitative and computational methods breathes fresh life into this vision. The current moment is no exception: advances in artificial intelligence and large language models have reinvigorated attempts to model society through the methods of social physics. This essay argues that these ambitions rest on a confusion about the nature of social complexity. Drawing a distinction between systems complexity and anthropological complexity, we identify the limitations of analyzing society as a complex system akin to natural systems. One crucial obstacle for social physics is the analysis of meaning and interpretation. Generative artificial intelligence (GenAI) offers important opportunities to model language and reflexivity, inviting and perhaps compelling social physics to confront meaning and interpretation as constitutive features of the social world. At the same time, GenAI leaves untouched and may even aggravate social physics’ failure to grasp power, institutions, and inequality.
Between 2024 and 2026, a variety of position papers and special issues in the field of social sciences have started addressing the social, epistemological, and cultural impact of generative AI. Such emerging literature looks vast, heterogeneous, fragmented, and expectably still at a pre-paradigmatic stage. Without the ambition of addressing such a fast-growing body of work in a fully systematic way, this paper reviews a significant variety of recent publications to identify major trends and some common ground aimed at bridge-building among different intra-disciplinary research strands, while remaining open to inter-disciplinary dialogue. It proposes a framework based on a few key concepts (AI as agency, communication, and memory), analytical dimensions (AI as object, practice, and method), and methodological tensions (macro-micro, quanti-quali, distant-close). In doing so, it contributes to the ongoing construction of a shared toolkit to investigate how various social and cultural domains have been affected by the recent development and increasing adoption of generative AI.
Contemporary consumer culture is increasingly shaped by a tension between calls for environmental sustainability and the persistence of status-driven, acquisitive consumption. While practices of voluntary simplicity are often presented as ethical alternatives to excess, their social and cultural implications remain deeply ambivalent. This article examines three emblematic consumption practices — decluttering, forest bathing, and upcycling — to theorize the emergence of quiet luxury as a distinctive reconfiguration of simplicity within late-modern consumer culture. Drawing on practice theory, the analysis conceptualizes these practices as constellations of meanings, materialities, and competences through which sustainability, moral value, and social distinction are co-produced. Empirically, the article combines discourse analysis of heterogeneous sources, including books, media coverage, brand communications, blogs, and digital platforms such as Reddit. The findings show that, rather than displacing consumerist logics, these practices rearticulate status in subdued and reflexive forms, grounded in restraint, presence, craft, and embodied competence. Together, these cases reveal how sustainability is mobilized as a moral and aesthetic resource that reshapes, rather than resolves, social inequalities. The article concludes by discussing the implications of quiet luxury for contemporary debates on sustainable consumption, class, and moralized lifestyles.
This interview with John R. Logan reflects on more than four decades of contributions to urban sociology, from Urban Fortunes to contemporary research on segregation and spatial inequality. Logan retraces the intellectual influences that shaped his work, particularly Immanuel Wallerstein’s macro-comparative perspective and Paul Lazarsfeld’s quantitative methodology, highlighting how he combined structural theorizing with rigorous empirical analysis. Revisiting the legacy of the Chicago School, he argues that theories should be judged not as right or wrong, but as more or less useful for addressing specific questions about urban life. A central theme of the conversation is the persistence of racial and ethnic segregation, examined through a long-term historical lens that traces contemporary inequalities back to the late 19th century. Logan emphasizes the importance of spatial scale, the interplay between state and market, and the value of mixed methods — especially the integration of ethnography with large-scale data. Ultimately, he defends the enduring relevance of urban sociology, insisting that the discipline advances not through data alone, but through the continual reframing of fundamental questions about inequality and the organization of space.
This commentary argues that the conditions for sociological analysis of AI technologies have changed. Previously, sociologists understood AI and “intelligence” as bounded systems and, as a consequence, the key question was how machines might mimic human judgment. Today, there is a different problem as AI infrastructures are now widely diffused, aligned with concentrated private ownership and state procurement. This development was antipicated and advocated by a central text for techno-libertarians but one that is little known within sociological circles: James Dale Davidson and William Rees-Mogg’s The Sovereign Individual (1997). This commentary returns to the core arguments of this text to consider what sociology can be “after AI”; in a world in which AI is now embedded in infrastructural frameworks that envelop the very institutions from which sociological critique can be launched.
Recent public and/or intellectual controversies around large language models (LLMs) forcefully demonstrate the relevance of language to social life. In both computer science and computational social science (CSS) research, their development has led to a surge of interest and engagement with the complexity of natural language and its relationship to culture. But what sociological understanding of language can this emergent research draw on? The rise of LLMs — and their use in social simulations of interacting “language agents” — thus renders salient the relatively minor importance attributed to language in many conceptions of social action. Before attempting to imagine a new “social physics” in the age of AI, a far more important task for sociology is to address its fundamental linguistic deficit, which has often been exacerbated by the influence of individualistic theories of action and/or language.
This paper critically explores how social inequalities shape the practice and politics of sustainable consumption. Drawing on a cultural and economic sociology approach and comparing contexts in the Global North (France) and Global South (India), we argue that sustainable consumption is an insufficient motor for eco-social transformation because it relies on and reproduces market inequalities via everyday practices. Dominant middle-class, performative environmentalisms exclude and stigmatize the poor and aspirational working classes, who often lack the surplus time and financial resources to participate in green consumption. As a result, they are unable to attain the moral recognition and social distinction associated with “enlightened” consumption. Rather than attributing this class polarization solely to the cultural dynamics of environmentalism, we argue that it is better understood as a consequence, and driver, of the broader social inequalities that underpin consumer economies — inequalities that are salient to how production is structured in globalized economies and to how markets are organized and communicate cultural values. We contend that this dynamic of inequality within consumerist societies has been largely overlooked in climate policy frameworks and public discourse, to its own detriment.
During the last few decades, new areas of research have emerged at the interface between the social sciences and computational sciences, such as computational social science and digital sociology. While there is significant willingness to engage across disciplines to establish these areas of inquiry, there equally exists widespread disagreement about what foundational concepts and methodological principles should guide their development. In the trading zone (Galison, 2010) between social and computational sciences, new possibilities, conflicts, and fundamental questions arise. Some argue that the combination of big data and machine learning will finally make possible the discovery of social laws (Lazer et al., 2021). Others claim that the AI revolution will unleash a new era of sociological theory and will enable the development of a qualitative computational science of society (Borch & Pardo-Guerra, 2024). In the 19th century, statistics and sociology found joint origins in the new methodology of social physics. In the wake of social media, attempts were made to reinvent this tradition, with mixed success (Watts, 2016). More recently, “AI” has been welcomed as an important opportunity for the renewal of interpretative social science (Friese, 2023; Törnberg & Uitermark, 2025). Can the wide uptake of machine-learning-based techniques across fields provide occasions to bring these debates and initiatives to maturity? Can we operationalize core questions and methodologies by combining social and computational theory and methods in new ways?
Contemporary accounts of consumption and sustainability are often criticized both for ignoring the non-Western world and for not paying sufficient attention to the structuring effects of capitalist political economy. This paper suggests a way to do both through a variegated consumer societies framework. Such a framework involves a multi-scalar approach to production-consumption complexes, including attention to both everyday life and consumption environments, as well as governance, production networks, and broader consumer-cultural frames. A central point is that capitalist consumer societies always take similar but varied shapes, depending on local institutions and cultures as well as the multi-scalar connections behind goods, trends and fashion. In this article, I use this framework to study changing consumption in China and Vietnam, two countries that have been among the fastest-growing economies and home to some of the fastest-expanding consumer-class populations globally over the past decades. I study the emergence of capitalist consumer cultures in these nominally socialist countries, zooming in on sustainable consumption and new forms of distinction through two core consumption domains: mobility and food.
Duncan Watts’ work has helped define how we study social relations, collective dynamics, and digital platforms. It ranges from articles combining formal modeling and empirical discoveries to widely read books. He has moved between disciplines and problems, shaping the fields of network science and computational social science along the way. In this conversation with Philipp Brandt, he retraces his steps and maps out an integrative framework that flips the research design process to start with problems and produce generalizable knowledge.
Machine learning has reinvigorated longstanding ambitions to develop a predictive science of society. This essay argues that enthusiasm for ML as a turning point rests on a category confusion between two distinct scientific imaginaries: Laplace’s deterministic universe, in which sufficient data and computational power yield precise prediction, and Comte’s social physics, which sought descriptive regularities rather than causal mechanisms. ML’s insertion within the dominant causal frameworks that characterize much quantitative social science implies, in particular, that the Comtian promise is far from reality on the ground. The essay explores three reasons why ML is unlikely to deliver a fundamental epistemic break: the performative instability of social classifications, the irreducible indexicality of data and claims, and the irreducibility of collective knowledge to information underdetermine ML’s overall capacity.
Drawing on the case of Kurdish and Turkish migrants and their descendants in the Ruhr Region and London, this paper argues that the Mixed Embeddedness Approach (MEA) would benefit from a conceptual extension to include historical and transnational dimensions, in addition to its established focus on institutional and social embeddedness. Despite the different institutional and social contexts in Germany and the UK, the findings demonstrate that these groups have developed remarkably similar entrepreneurial patterns, resulting in analogous ethnic economies in both countries. This convergence is not coincidental but is instead shaped by historically contingent processes such as neoliberal restructuring, deindustrialization, economic crises, migrant unemployment, demographic concentration, and evolving migrant needs since the 1970s, which have reshaped opportunity structures over time. Furthermore, the entrepreneurs’ transnational activities, including investments abroad, sourcing from external markets, and establishing cross-border customer relations and networks, have contributed to shaping this opportunity structure. Rather than treating transnationality and historical change as external or secondary factors, the paper argues for their integration into the MEA as constitutive dimensions of opportunity structures. In doing so, the paper frames MEA as not merely a combination of social networks and institutional contexts, but as a dynamic, processual framework attuned to historical transformations and transnational linkages. This historically contextualized MEA provides a more comprehensive perspective on ethnic/migrant entrepreneurship, opening new avenues for research and policy development in this area.
This article reinterprets Carlo Ginzburg’s indiciary paradigm as a general theory of knowledge production and connects it to contemporary debates over generative artificial intelligence. In line with Ginzburg, I posit that we cannot directly access unmediated social life. But rather than treat distance as an obstacle to knowledge, temporal, epistemic, and perspectival forms of distance are its enabling conditions. We can make sense of these by weighing positive and negative analogy transfers (Mary Hesse) between radically different form of knowledge traces. Consider for example archival records, or the outputs of in silico research. Both domains require reasoning from traces that stand in for absent realities. Yet, synthetic outputs derive their authority from optimization and their plausibility is operational, rather than referential. A case study of Nazi racial science clarifies what is at stake when AI systems are treated as stand-ins for social actors, and shows how perspective can be abstracted from subjecthood and redeployed instrumentally: the extraction of epistemic resources without reciprocity, and the obscuring of production processes. I introduce the concept of in silico perspectivism to name a reflexive methodological stance adequate to this moment.
