Scholarship on science has long contained a tension between accounts emphasizing convergent forces and those highlighting divergent practices. This article addresses that tension through an ethnographic study of data science. It advances the concept of epistemic idiom-a recognizable and portable pattern of practices that evolve alongside knowledge-making tools, analogous to musical idioms arising from the interaction of material affordances and learned techniques. Through analysis of a project attempting to sequence chromosomal centromeres, I show participants enacting the nomad idiom of data science: a data-led approach involving tools for iteration and exploration, cross-disciplinary borrowing, bricolage construction of bespoke assemblages, and the motivation to scale. Though the project failed to sequence centromeres, it generated unexpected discoveries, challenged established theories, and yielded lasting changes to scientific practice. The case reveals how epistemic trends can spread without being totalizing-the nomad idiom enabled both convergence (shared technical toolkit) and divergence (local adaptations, surprise outcomes) simultaneously. The epistemic idiom concept complements existing STS frameworks by explaining how practices can migrate across epistemic communities, while those communities retain distinct cultures. It speaks to concerns over computational universalism and epistemic monocultures, demonstrating that alternatives exist-ways for scientific trends to be borrowed rather than imposed, becoming remixes instead of replacements.

The use of AI-based technologies is reshaping mathematics in ways that extend but also strain the classic Big Science analogy. As in Big Science, the new AI-based mathematical work relies on concentrated computation, specialized and hierarchical teams, sociopolitical justification, and large-scale collaboration. But AI also changes how epistemic labor is distributed and attributed: Systems now generate constructions, explore proof spaces, and formalize arguments, prompting mathematicians to renegotiate how credit, authority, and accountability are assigned across hybrid human-machine assemblages. This reconfiguration redistributes epistemic authority toward corporate labs and elite hubs that control models, data, compute, and narrative, thereby gating which problems appear tractable and legitimate. Analyzing four dimensions (resource concentration, division of labor, political embedding, and collaboration), and examining recent cases, we argue that AI-based mathematics constitutes a novel sociotechnical regime we call 'Big AI Mathematics', in which truth claims are increasingly mediated by opaque pipelines and institutional power, making the future of mathematical knowledge as much a governance problem as an epistemic one.

This paper draws on two affectively charged fieldwork encounters in a Swedish daily activity centre for persons with profound intellectual and multiple disabilities (PIMD) to explore how affect intervenes in fieldwork and reconfigures what becomes knowable. Situated within Science and Technology Studies (STS), the analysis reflects on how moments of discomfort, intimacy, and apprehension unsettle a research orientation initially shaped by material semiotics, prompting renewed attention to questions of complicity and the epistemic terms of practice-oriented analysis. Rather than treating affect as noise or private response, it is approached as a register through which broader histories of segregation and exclusion become palpable in the encounter. Tracing how affect shapes what is registered as relevant or knowable, the paper develops epistemic adhesion as a conceptual companion to disconcertment-understood here as moments of felt epistemic disruption-to name the ease or alignment through which dominant orientations are affirmed rather than troubled. In attending to how disability is rendered affectively and epistemically available, the analysis offers a situated reflection on the stakes of knowing-and not knowing-with PIMD.

Natural Language Processing (NLP) technologies-ranging from topic models to today's large language models like GPT-have rapidly entered the social sciences, reshaping methodological practice. Yet researchers often overlook the stark political-economic contrasts between academia and the AI research-industry symbiosis. Identical algorithms, once embedded in different institutional settings, acquire different meanings and standards of evaluation. This paper shows the divergence by examining topic modeling, a classical NLP technique in computational social science. Social scientists grapple with the instability of applying topic models to the same corpus, whereas in the AI industry such variability matters little, given different evaluative priorities. Through a comparative analysis of topic modeling's trajectory across AI and social science, I show how organizational contexts and goals shape the development of the same algorithms, and why framing instability as a purely technical issue is problematic in the social sciences. The findings reveal that algorithms are not simply technical tools but products of material political-economic regimes. Recognizing this, I argue that STS scholars have a vital role to play in computational social science: not only by critically examining and developing methods, but also by interrogating the material-political-economic regimes in which algorithms are enacted, and by working toward more just alternatives.

Ghostwritten medical papers are a cornerstone of pharmaceutical marketing. We trace how one manuscript written under the instructions and supervision of opioid producer Mallinckrodt was produced and eventually published in a medical journal to present a marketing message. We systematically analyse an archive of internal communications made public after litigation through the lens of Ricœur's work on imaginaries. Along his first analytic axis, we show how the ghostwriting process shapes and reshapes identities, ascribing agency and portraying social order. Along the second, legitimacy was constructed by the expert voice of a peer-reviewed article 'authored' by researchers, but written by backstage medical writers. Third, the result was a twisted phantasmagory of both academic writing and pain management. Professional medical writers generated an imaginary of pain management to expand the prescription of opioids by redefining pain categories and reassigning agency and responsibility of actors in pain treatment. Meanwhile, backstage writers also reproduced an imaginary of scientific writing, with efficient writing as an editorial service that misrepresents authorial contribution and responsibility. We suggest that Ricœur's analytic scheme could also be used to disentangle technoscientific imaginaries in other settings.

A trend towards more and larger research collaborations in the sciences raises questions about how individual scientists who work primarily in collaborations receive recognition. Experimental high-energy physics is known for exceptionally large research collaborations, with publications featuring thousands of authors in alphabetical order. Consequently, the question of how individual scientists receive recognition has been explicitly discussed in this discipline. In this article, I describe discussions and efforts to improve the recognition of individual researchers in the ATLAS collaboration at the European Laboratory for Particle Physics (CERN), based on an analysis of documents and interviews with collaboration members of all career stages. I analyze these accounts by introducing a conceptual framework distinguishing between elementary and institutionalized recognition, and recognition as appreciation and admiration, respectively. I show that articulations of recognition are mobilized by the collaboration to reproduce the experiment and the community. Drawing on these observations, I argue for a broader concept of recognition in the social studies of science. Beyond the notion of recognition as symbolic capital for individuals, we should also consider the reproductive and stabilizing capacity of recognition for scientific communities. I also show how receiving community-oriented appreciation creates a dilemma for early-career researchers who desire admiration to compete in academic labor markets. The need to value a broader range of contributions in collaborative research therefore requires structural changes in academic career systems.

Medicine has undergone a major shift in its dominant mode of clinical reasoning, a Kuhnian paradigm change centred on diagnostic method rather than disease theory. Until the mid-20[th] century, clinical practice followed an experience-based model in which clinicians acted as detectives, assembling symptoms and signs as 'clues' to infer underlying pathology. This Holmesian style of reasoning relied on tacit judgment acquired through apprenticeship. In the mid-20[th] century, this paradigm entered a crisis of confidence as empirical studies revealed extensive variability and error in clinical observations. These findings undermined faith in intuition and professional mystique. In response, medicine increasingly adopted numerical indexes, decision trees, flow charts, protocols, and guidelines. These tools replaced the spatial assemblage of clues with formalized, conditional 'if-then' logic that linked measurement directly to outcomes and action. The clinical algorithm has become the key exemplar of a new algorithmic paradigm, one that underpins contemporary evidence-based medicine and risk-factor management.

Petroleum products silently govern human relations not only through ideology or policy but through their chemical composition and material form. What is the technical-chemical work that translates geological formations of oil into sociopolitical formations of human relations? How is governmentality encoded into petroleum products during their manufacturing? This article develops the concept of chemical governmentality to analyze how engineering processes in oil refineries govern sociopolitical life. Drawing on ethnographic research in Indian refineries and households, I examine how fuels such as kerosene, Liquefied Petroleum Gas (LPG), or Piped Natural Gas (PNG) are chemically and infrastructurally configured to produce distinct forms of domesticity, sociality, and state-citizen relations. Refining processes, product specifications, and testing protocols embed political logics into petroleum products, disciplining consumers through material means. Chemicals cohabit human worlds not just through toxicity, but also through governmentality. My analysis foregrounds chemical engineering as an arena where material machinations participate in the production of everyday government.

Since the 1990s, Taiwan has promoted genomics as part of its shift to a knowledge economy. However, controversies concerning Indigenous genetics and genomics extend into political, sociocultural, and historical dynamics. This has prompted a movement among Taiwan's Indigenous activists, culminating in legal reforms that made Taiwan the first country to enact legislation mandating collective informed consent for Indigenous peoples in 2016. This article proposes the concept of 'differentiated biological citizenship' to explain how Taiwan's Indigenous peoples have developed bioethics governance. First, the article analyzes the origin, dynamics, and main events of this controversy, arguing that it should be understood in the context of globalization and Taiwan's evolving identity politics. Second, the article traces how different actors (scientists, Indigenous elites, and government officials) negotiate a series of actionable strategies and develop collective informed consent based on the law. Finally, examining such issues as the relationship between trust and distrust and the power inequality between Indigenous and Han peoples, this article also investigates the difficulty in practicing collective consent as a governance model for Indigenous communities.

Citizen science (CS) is often characterized as involving inherent trade-offs between scientific validity and public engagement. While existing literature has documented these competing imperatives, less is known about the concrete organizational mechanisms that enable their coexistence. Based on ethnographic fieldwork at the Israel Center for Citizen Science (ICCS), this article examines how hybrid knowledge production is sustained not by resolving this tension, but by structuring it. ICCS functions as a unique scientific site where the primary 'instruments' are communities of human participants. Through analysis of validation processes (mechanical objectivity, standardized protocols) and engagement practices (role-switching, boundary work), I demonstrate that the validity-engagement tension acts as a mechanism rather than a hurdle. The analysis reveals a structure of 'fractal biopower'-nested power/knowledge relations where the division between subject and object repeats at every scale: Nature is subject to participants' observation, participants to institutional management, and institutions to scientific evaluation. This recursive structure allows contradictory logics to operate simultaneously, suggesting that contemporary science increasingly functions through the management, rather than the purification, of hybrid tensions.

This article tracks the origins, developments, and current usages of the concept of actionability in precision medicine. Originally a label for highlighting 'incidental' findings from genetic research, over time this concept became ascribed to catalogued genetic 'variants' that are both potentially pathogenic and 'druggable'. Drawing on a qualitative document analysis, the article identifies several clusters of relevant actors promoting actionability, and several developments to which the concept was subject over time. The concept answers to various problems that emerged gradually, at different points in time, and partially as consequences of previous solutions: the translation between genomic testing in research and clinical settings, the problem of overdiagnosis resulting from a technologically induced information overflow, the complexity of test results combining various dimensions of meaning of non-linearly linked variables, and the problem of having to 'personalize' a therapy selection while integrating a new logic into a pre-existing health system.

How do emerging environmental phenomena become actionable amid scientific uncertainty and public urgency? This article argues that to understand how microplastics become governable, we must study classification as a central site where matters of fact, concern, and care are composed. Drawing on comparative assemblage ethnography across scientific conferences, public debates, and European regulatory processes, we trace how microplastics are enacted as epistemic, public, and regulatory objects. In scientific practice, object-centered classifications aim at rendering heterogeneous particles measurable and comparable. In public arenas, labeling condenses plurality into a recognizable and affectively charged issue. In regulatory contexts, source-centered and legally operational categories reorganize the phenomenon into governable segments. These are not translations of a stable object-microplastics-but distinct enactments shaped by different evidentiary standards and institutional constraints. The case shows that classification under conditions of ontological instability does not necessarily lead to closure but enables coordination. We show how governance materializes through classificatory infrastructures that stabilize relations between fact, concern, and care without resolving epistemic indeterminacy. Classification is a political technology through which environmental objects are rendered actionable in the absence of settled knowledge.

Citizen science (CS) is met with a range of expectations regarding trust-building that originate in policy frameworks and academic studies. These tend to understand trust as an outcome of participatory research. Adopting a different approach, our praxeological study examines trust as a context-dependent mechanism. Drawing on qualitative interviews, surveys, and document analysis, we analyze the experiences of project leads and volunteers in two contrasting German CS initiatives. Our comparative case analysis reveals that trust should be understood not as a desirable outcome, but as demands arising from collaboration objectives and contextual factors, and as practices that entail specific commitments from the actors involved. The contrast between standardized data collection in natural science and interpretative methods in health and educational research shows how methodological frameworks generate distinct trust demands. Examining research settings through the lens of trust while considering resource and power constraints exposes tensions between objectives for inclusion, knowledge production, and knowledge transfer. Moreover, interpersonal trust is built partly through critique of conventional research practices, challenging whether generalized trust in CS or in science constitutes an appropriate analytical frame for these situated practices.

Trap-Neuter-Return (TNR) is widely promoted as a humane technique for managing free-roaming cats. Yet its legitimacy remains fiercely disputed. Drawing on four months of participant observation and two years of follow-up fieldwork in Tenerife (Canary Islands, Spain), this article re-situates TNR as an assemblage whose coherence is always provisional. We use Actor-Network Theory (ANT) to trace the translations that align heterogeneous actors, while foregrounding the relational, ethical, and affective dimensions of human-cat interactions that sustain (or undo) that alignment. Empirically, we examine how legal statutes, technical guidelines, traps, smartphone chats, ear-tipping, municipal ordinances, veterinary scalpels, feeding routines, and cats themselves operate as co-constitutive actors. Spain's 2023 Animal Welfare Law tried to fix TNR as national policy, but in Tenerife's biodiversity hotspot the statute collides with invasion biology data and local politics. Five sites reveal Callon's translation in action: legal paperwork, licensing cards that recast feeders as 'colony managers', chat-based surgery logistics, the operating room where policy meets flesh, and a fishing village stalled by a trap-savvy queen. Rather than assessing TNR's effectiveness in reducing cat populations, we highlight its emerging networks of heterogeneous more-than-human actors and the everyday, situated labor required to hold it together. In doing so, the article shifts STS debates toward the everyday practices that enact and maintain assemblages.

Accounts of scientific work at the lab bench largely overlook or sideline the considerable time and effort that scientists devote to the acquisition, management, and dispersal of funding. While multiple studies explore the subject of scientific funding from the political economic, historical, and comparative policy perspectives, we develop an approach premised upon relational socioeconomics and grounded in ethnographic study at the bench. We focus our attention upon what we call 'money-work'-scientists' everyday practical work, action, and interaction about funding. To illuminate how scientists regularly engage in mundane activity that is simultaneously scientific and economic we apply Zelizerian concepts of earmarking, making good matches, and the development of valuation criteria. Our framework for contemporary laboratory ethnography investigates a political economy of science funding in which science and money do not occupy 'separate spheres' or 'hostile worlds', but rather lead intimately connected lives. Given the current crisis in science funding, such a perspective on relational work, currency fluctuation, and valuation in exchange is essential for the next generation of science studies scholarship.