Steven J. Jackson

Steven J. Jackson

This paper offers a concept of working relations as a complement and extension to existing theories of maintenance, care and repair. Building on the cases of an umbrella, a tractor and a pond, it advances seven propositions that might guide and inform further work and thinking in this space. It concludes with the challenging figures of Chernobyl, nickel extraction, and AI, and argues for the centrality of working relations to more generative and pluralistic relations with the things and worlds around us.

Ritik Batra, Lydia Kim, Ilan Mandel et al.

Craft practices rely on evolving archives of skill and knowledge developed through generations of craftspeople experimenting with designs, materials, and techniques. Better documentation of these practices enables the sharing of knowledge and expertise between sites and generations. However, most documentation focuses on the linear steps leading to final artifacts, neglecting the distinct tacit knowledge, improvisational actions, and situated adaptations needed to meet the unique demands of each craft project. This omission limits knowledge sharing and reduces craft to a mechanical endeavor, rather than a sophisticated and contextual way of seeing, thinking, and doing. Drawing on expert interviews and literature from HCI, CSCW and the social sciences, we develop an elementary grammar to document improvisational actions of real-world craft practices. We demonstrate the utility of this grammar with a MLLM-powered interface called CraftLink that can be used to analyze expert videos and generate documentation to share material and contextual variations of practices with other knowledgeable but non-master craftspeople. Our user study with expert crocheters (N=7) evaluates our grammar's effectiveness in capturing and sharing expert knowledge with other craftspeople, offering new pathways for computational systems to support collaborative archives of knowledge and practice across time, space, and skill levels. We conclude by showing how our grammar address four key tensions of the craft learning environment: personal and shareable documentation, fragmented and discoverable expertise, linear and iterative practices, and data privacy and ownership.

Daniel Mwesigwa, Steven J. Jackson

This chapter develops the concept of moral orders of repair, defined as the specific norms, rules, values, and expectations that structure and support joint work and exchange in repair worlds and other spheres of collaborative practice. Drawing on ethnographic fieldwork in mobile phone and computing repair markets in Kampala, Uganda, we identify three key dimensions of moral orders: fair exchange, collaboration across hierarchy, and relational accountability. We show how moral orders provide a detailed specification of informal rules gestured as implicit in moral economies, and how these rules inform the practical and ethical work of repair.

Samir Passi, Steven J. Jackson

The trustworthiness of data science systems in applied and real-world settings emerges from the resolution of specific tensions through situated, pragmatic, and ongoing forms of work. Drawing on research in CSCW, critical data studies, and history and sociology of science, and six months of immersive ethnographic fieldwork with a corporate data science team, we describe four common tensions in applied data science work: (un)equivocal numbers, (counter)intuitive knowledge, (in)credible data, and (in)scrutable models. We show how organizational actors establish and re-negotiate trust under messy and uncertain analytic conditions through practices of skepticism, assessment, and credibility. Highlighting the collaborative and heterogeneous nature of real-world data science, we show how the management of trust in applied corporate data science settings depends not only on pre-processing and quantification, but also on negotiation and translation. We conclude by discussing the implications of our findings for data science research and practice, both within and beyond CSCW.

Samir Passi, Steven J. Jackson

Learning to see through data is central to contemporary forms of algorithmic knowledge production. While often represented as a mechanical application of rules, making algorithms work with data requires a great deal of situated work. This paper examines how the often-divergent demands of mechanization and discretion manifest in data analytic learning environments. Drawing on research in CSCW and the social sciences, and ethnographic fieldwork in two data learning environments, we show how an algorithm's application is seen sometimes as a mechanical sequence of rules and at other times as an array of situated decisions. Casting data analytics as a rule-based (rather than rule-bound) practice, we show that effective data vision requires would-be analysts to straddle the competing demands of formal abstraction and empirical contingency. We conclude by discussing how the notion of data vision can help better leverage the role of human work in data analytic learning, research, and practice.