In a rapidly transforming technological landscape, higher education institutions struggle to meet the demands of industry in providing timely and appropriate upskilling and reskilling opportunities for the workforce. Under the umbrella of “advanced digitization”, a term prominent in European discourse, covering a wide range of new digital technologies within the Industry 4.0 and Industry 5.0 paradigms in manufacturing, ICT and engineering, we use scoping review methods to explore the unique challenges and interventions in this area, highlighting potentials for further research. Our scoping review examines 81 peer reviewed Scopus-indexed papers, outlining the current global research on the challenges and interventions in upskilling the workforce for advanced digitization. Our results show that research in this area is most prevalent in Europe, North America, and Asia and is most frequently done within the manufacturing and ICT sectors. Due to the fast pace of advanced digitization, close collaboration between education providers and industry is required to develop relevant and appropriate upskilling opportunities. We underline the importance of developing strategic management and change management knowledge alongside technology skills. We also highlight the importance of inter- and trans-disciplinary knowledge development. While determining skill needs has attracted substantial research focus, businesses may struggle with developing strategic upskilling plans despite being aware of their skills needs. We conclude that interventions that foster situated engagement and hands-on experience are vital for the future of upskilling.

The social sciences and humanities have increasingly adopted computational terminology as the organizing categories for inquiry. We argue that by organizing research around vernacular computational objects (e.g. data, algorithms, or AI) and divided worldly domains (e.g. finance, health, and governance), scholars risk obscuring the universalizing practices and ambitions of computation. These practices seek to establish new relationalities at unprecedented scales, connecting disparate domains, circulating resources across boundaries, and positioning computational interventions as universally applicable. Drawing on intellectual traditions that inspect the fixity of universalizing claims, we problematize the easy adoption of computational categories and argue that they serve as epistemic traps that naturalize the expanding reach of computational universalism. Instead of accepting the hardened categories of our interlocutors, we propose attending to the partial, effortful, and often contested work of translation and commensuration that enables computational actors to position themselves as obligatory passage points across all domains. This approach reveals not only the remarkable achievements of computational relationalities at scale but also their exclusions, betrayals, and partialities. Our intervention aims to spur perspectives that examine how computational actors parse both technical objects and social worlds to advance universalizing ambitions while simultaneously obscuring the enormous labor required to maintain these divisions and connections.

This article reflects on how sociology can analyse the role of artificial intelligence (AI) in scientific practice without buying into the current AI hype. Drawing on sensibilities developed in actor-network theory (ANT) it introduces the concept of agencing (agency as a verb) which refers to how scientists debate and configure the human and machine agency. It suggests that we can come to a more nuanced understanding of the effects of AI in science by attending to actors’ agencing practices. By discussing three ideal types of agencing, the article argues that AI should not be regarded as a rupture in the tooling and practices of science, but rather as a continuation of long-standing patterns of practice. That is, agency, and the space for action and judgement, is organised differently in the AI-driven laboratory; however, this is not a new configuration of epistemic agency. Rather we might understand these changes as building on statistical epistemic configurations going back to the birth of statistics in sociology in the 1700s and 1800s.

This article develops an analytical and methodological field guide for studying the mundane practices that constitute machine learning systems. Drawing on science and technology studies (STS), I move beyond the opacity/transparency dichotomy that has dominated critical algorithm studies to examine how machine learning is assembled through everyday work. Rather than treating algorithms as black boxes or magical entities, I focus on four empirical moments of translation—feature extraction, vectorization, clustering, and data drift—where technical work becomes political choice. By ethnographically attending to practitioners' tinkering, negotiations, and valuation practices in these moments, we can trace how classification systems are constructed and stabilized. This approach allows us to ask: How are particular features of the world selected as relevant for prediction? Through what practices are people and phenomena translated into mathematical vector spaces? How are temporal assumptions encoded in data? By studying these mundane processes of construction, we can understand how machine learning systems enact particular ways of seeing, classifying, and predicting the world. This field guide thus contributes methodological tools for analyzing how the politics of machine learning is assembled in practice, opening analytical space for critical engagement beyond calls for transparency or fairness.