
https://www.cambridge.org/core/books/how-data-need-people/776A73CBBE3EAA67243A4830514F1F62
Grey Gundaker: Although sociology and history of science have been around a long time, it took a while before anthropologists moved into this arena. What, if anything, do you think anthropology adds to or tackles differently from other disciplines?
Götz Hoeppe: This is a great question. One of my favorite quotes is Tim Ingold’s notion that “anthropology is philosophy with the people in.” It goes a long way and explains its difference to much history and sociology that deal with people, too, but often not with the real life of their practical reasoning and acting. Sociologists of science had long focused on bibliometrics and, following Robert Merton, on the normative structure of science. Back in the 1970s, when sociologists Bruno Latour and Karin Knorr Cetina first ventured into scientific laboratories to study knowledge-making practices there, they declared to follow an anthropological approach and did ethnography. Knorr-Cetina, for example, wrote that this meant to look at laboratories with the “innocent eye of the traveller in exotic lands.” Of course, anthropology has much more to offer than ethnography as a method. (And, like linguistic anthropologist Jan Blommaert, I think of ethnography less as a method than as a paradigm that is imbued with its own epistemology and ontology.) But anthropology’s openness toward surprises that Knorr-Cetina alludes to continues to be important. Facing unknown domains, anthropologists are often wary that diverse actors, agencies, and objects may matter to a social setting, not all of which are human. They cannot know in advance which forms social life may take, and if these actors and agencies are members of a culture or community. In the book I focus on what I call data-centric socialities: forms of social coordination unfolding in the making, use, publication, and reuse of digital scientific data. These involve scientists and technicians, students and their supervisors, teams that collaborate and compete, makers and users of data – and always also the technologies and media that they use together.
In putting data and social practices at the center my study, I draw on another strength of anthropology: its attention to sensemaking, embodiment, materiality, and language as essential to a community’s social and cultural life. By focusing on interactions of data makers and data users, I discovered a subtle ethical landscape that is consequential for those affected, but unrecognized by earlier studies. And I noticed that I was not the only ethnographer around: scientists and technicians undertook social inquiries themselves. This is an important practice that novice scientists need to learn. What also fascinated me was how scientists use diagrams as sites for cultivating data, and how their collaborations are organizational experiments for managing their work with large datasets. Probing into the conversations among members of a research team helped me to gain insights into the normative expectations of what people call open science. In this way I also learned that scientists commonly presume what Alfred Schütz called a “reciprocity of perspectives”: they assume that data users act like themselves as they seek to encode their knowledge in a dataset and design it for others to use it properly.
This focus on language and social action led me to matters of social accountability. In fact, accounting practices became my book’s central thread. These practices are implicated in everything I told you so far. The social study of accounting practices begins with a classic ethnography, Edward Evans-Pritchard’s 1937 Witchcraft, Oracles and Magic among the Azande. It sounds like an odd inspiration for a study of contemporary scientists, but how the Azande used their oracles and dealt with their often-contradictory outcomes is, in fact, very productive for making sense of scientists’ data practices. Both the Azande and contemporary scientists are concerned with securing membership in their communities and living in a shared world. Another study that I found enormously useful is Larry Wieder’s classic account of the convict code that commits the residents and staff of a half-way house for convicted narcotics offenders to certain ways of communication. This may sound a bit curious as well, but I hope it all makes sense when you read it in context. Scientific data that are good to be used, circulated and reused emerge from accountable social relations.
Grey Gundaker: The main approach you use to study astronomers at work is ethnomethodology, which Harold Garfinkel pioneered to investigate astronomy as a “discovering science.” Why did you choose this path as well? And what additions, changes, or amplifications did you make during your own research in order to make a good fit with current practices in astronomy?
Götz Hoeppe: I discovered ethnomethodology only after my main period of fieldwork. I should add that I am an astronomer-turned-anthropologist: I did a Master of Science in physics with a thesis on radio observations of galaxies and then a PhD in environmental anthropology with an ethnography of a fishing community in Kerala (India). Among fishers and astronomers, I followed people around as they pursued their business and made sense of what they encountered, often collectively. In doing so I documented what ethnomethodologists call “member’s methods.” Only later, when I wrote up my first paper on the project, did I discover that I had adopted elements of an ethnomethodological approach without realizing it.
Incidentally, by having been trained in the science that I then examined I fulfilled what Garfinkel called the “unique adequacy requirement.” He argued that this is needed for studies of expert work. But I also discovered Garfinkel through the anthropologist Mary Douglas, a student of Evans-Pritchard, who took him seriously in the study of accounting practices. Garfinkel and Douglas both learned a lot from Durkheim.
For me, discovering ethnomethodology was a stunning experience. It surprised and fascinated me like no other anthropological or sociological writing before or since. It is an incredible tool to discover how people make sense, how social practices unfold and how we produce social order. But not everything that I witnessed can be seen through an ethnomethodological lens. When you call astronomy a “discovering science” you hint at Garfinkel’s famous pulsar paper, in which he examines a scientific discovery by scrutinizing an audio tape recorded by Steve Woolgar during part of their work. Reading this fascinating article felt as if my brain was being rewired. It is an amazing text. But I do not agree with Garfinkel’s claim that the discovery was constituted and accepted only through the local work that he describes. Historical context often matters in ways that Garfinkel ignored. This is one thing I add in my study. In addition, I pay more attention to documents and media and adopt a more inclusive understanding of accountability. These are some means to bring the local and the distant into a singular account, which I wanted, given that data made at one site are often used at diverse other sites.
Grey Gundaker: “Observation” and “discovery” are terms that the public generally associates with astronomy, physics, and other sciences. Yet these terms can suggest an overly simplistic view of the process– as if the scientist merely sets up and experiment or designates a space or entity to observe and… bingo… new information emerges. Discuss some of the ways that technological and human interactional mediation shape the process of investigation as it actually occurs.
Götz Hoeppe: Indeed, textbooks often describe scientific observation as a sequence of calibrating your equipment, next experimenting or observing, then analyzing what you measure and, eventually, comparing it with the theory. But, in practice, science does not quite work like this. In my book, it is Nadine (pseudonym), who experiences this painfully. I describe her training as a graduate student in Chapter 3. Nadine was meant to combine data from different sources for analysis. This is a common problem of data-rich science, but often also a challenge. Nadine had been meticulous with her data calibrations and when she moved to her analysis, she was thrilled, thinking that she had made an important discovery right away. But Otfried, her supervisor, remained skeptical and asked her to re-calibrate her data. This is what Nadine did – and her discovery disappeared. As she moved on, this happened again. This back-and-forth between calibration and doing science eventually produced a dataset that Otfried and other scientists were happy with and that did, in the end, produce new results. So, Nadine had to suffer through moments of exasperation and frustration. But along this way it was not only her dataset that got calibrated: Nadine herself became a member of a community and culture. Nadine learned to laugh about jokes that only insiders get and to talk about science in ways that outsiders would find as lacking important details. Calibrating data and doing science are essential to becoming a scientist.
Grey Gundaker: Your book also vividly portrays the many layers of contingency involved in astronomers’ work. These include dependence on outside funding and limited telescope time, the hierarchy and distribution of roles among the astronomers, and the complex checks and balances involved in making sense of data sets and rendering them comprehensible to other users. Tell us about how some of this works.
Götz Hoeppe: You are right. Well, in our daily lives we all deal with many contingencies. We miss the last bus and must find another way home. We go shopping, but our favorite salad dressing is out of stock. Likewise, scientists cannot but live in a less-than-perfect world, even though descriptions of science rarely say so. Nadine and her fellow graduate students had to abandon the confidence they had as undergraduate students: that there always is an exact solution to every problem. As elsewhere in life, they now had to find answers to the question, “what is good enough?” and “is this good enough?” Things like working toward a deadline, technical malfunctions or clouded out observing nights lead to smaller datasets than desired, forcing scientists to make hard decision. There are often several contingencies at the same time. What I describe in the book is scientists’ social practice – one may say: social art – of dealing with such contingencies, and doing so creatively and resourcefully. Of course, data may be lacking and projects may fail, but in the work that I witnessed, good enough also means that their work, which is open for criticism and reassessment, remains accountable to others, and is often successful because of people’s artful accommodation of social accountabilities.







