Bo Yun Park (University of California, Berkeley) interviews Angèle Christin (Stanford University) about her book, Metrics at Work
Tell us a little about yourself, your research, and any info on your future career plans you’d like to share.
These days I usually define myself as an ethnographer of work and technology, mostly because I spend a lot of time with computer scientists and engineers in Silicon Valley, so the methodological angle is easier to read across disciplinary lenses. But of course, my training in cultural sociology informs everything I do.
I study how algorithms and analytics are changing work practices and professional identities. I look at how people use and make sense of the platforms, metrics, and computational systems in their daily work. And I try to figure out what is changing – and what’s not changing – when these systems become increasingly prevalent in our societies.
I’ve examined these questions in several ethnographic sites, including web journalism (my book Metrics at Work: Journalism and the Contested Meaning of Algorithms, was published in 2020 by Princeton University Press), criminal justice, and now the brave new world of social media creators. Over the past two years I’ve been conducting virtual ethnographic fieldwork with influencers and marketers. I’m in the middle of writing a book based on this material, which is making me really happy (PhD students: there is a life after the dissertation and first book, and in my experience it’s more fun!).
I just came back to the Bay Area after a year of (pandemic) sabbatical, first at the Paris Institute for Advanced Study/Sorbonne Université, and then as a Visiting Researcher with the Social Media Collective at Microsoft Research New England. I’m excited to be back at Stanford (where I’m an Assistant Professor in the Department of Communication) and hang out with colleagues across campus. This year I’m affiliated with the Center for Work, Technology, and Organization in Management Science & Engineering at Stanford – a great place to observe how Silicon Valley is evolving.
How does cultural sociology influence your thinking and research?
This is such a hard question – in part because culture is everywhere! During my training, I always gravitated towards sociology of culture: first in Paris, at the Ecole Normale Supérieure, where I pretty much circled around the holy trinity of Bourdieu, Boltanski, and Latour, and then at Princeton, where I absorbed everything Paul DiMaggio, Viviana Zelizer, and Kim Scheppele would teach.
But if I think a bit more analytically about the role of culture in my current research, I would say that it comes in two main flavors.
First, I am interested in how computational technologies and the metrics they provide become intertwined with cultural dynamics of valuation and evaluation. Sociotechnical systems cement some of these dynamics, but there is always some room for negotiation. These uses and interpretations in turn are shaped by a range of structural factors – field structures, organizational and professional norms, habitus and dispositions, and so on. In my work, I examine when and where these different forces come into play and shape the effects of algorithmic technologies on the ground. For instance, in Metrics at Work, I show how French and US journalists project different meanings onto clicks, traffic metrics, and what I call their “algorithmic publics.” I argue that these differences are shaped by the trajectories of the journalistic field in the US and France, as well as by the organizational cultures of the newsrooms I studied. Pushing back against determinist accounts of technological convergence in the digital age, I show how journalists can reproduce cultural difference when they interact with algorithms and analytics.
Second, I like to think that my attention to culture comes through in my love for comparisons. A while back, there was a great ASA panel on “Audacious Comparisons.” This expression resonated with me… in part because I enjoy comparing things. Over the past ten years, and with the help of talented co-authors, I’ve compared newsrooms in the US and France; algorithms in journalism and criminal justice; predictive technologies in criminal courts and police departments; and personal branding among freelance journalists and jobseekers. Across these sites, I’m always focusing on the interplay between field-level structures, professional and organizational cultures, and sociotechnical systems. Comparisons are good to think with: they help me conceptualize similarities and differences, as well as tease out the culture/structure nexus across institutional sites.
How do you envision the future of cultural sociology?
Digital technologies are everywhere. Especially during the Covid-19 pandemic, everybody has been spending staggering amounts of time in front of screens, on a wide range of devices, websites, applications, and platforms. These technologies are mediating and reconfiguring a rapidly expanding number of domains – communication and information exchange through search engines and social media, obviously, but also transportation, shopping, care work, food, workplaces, politics, public administration, the arts, and so on. In fact, it’s hard to find a domain that’s not affected.
This process of digitization in turn is far from neutral. It comes with specific ideologies (including the so-called “Californian” one), business models (often described as the “platform” one), and data-hungry infrastructures that nudge users in specific directions (often leading to the reproduction and reinforcement of inequality). These changes also affect what people do and how they socialize offline: did you ever stop to watch influencers do choreographies for TikTok videos or Instagram shoots in a street or campus hallway? If you haven’t, check out the mesmerizing Instagram account “Influencers in the Wild.”
To date, most of these changes have been explored outside of sociology – in computer science, communication, information science, media studies, and so on. With important exceptions, sociologists are often missing from these discussions. Yet they have so much to offer! For instance, one pet peeve of mine is the generalized amnesia that permeates most studies of digital technologies. Mirroring industry dynamics, there is a scholarly race for covering each “new thing,” be it a new platform, cryptocurrency, or artificial intelligence. That’s fine if it helps people to get funding, but a lot of the structural changes we are discovering have been under way for more than two centuries, and it doesn’t hurt to return to Marx, Weber, or Simmel to get a sense of déjà-vu. I would love to see more cultural sociologists apply rigorous methods, carefully crafted theoretical frameworks, and long-term historical approaches to the study of digital technologies, because engineers and computer scientists really need to hear these perspectives. Right now, philosophers and economists are flocking to the study of “AI ethics,” “fake news,” and other digital evils. That’s fine, but I wish there were more cultural sociologists at the table as well!
What is one piece of advice you have for graduate students or early-career sociologists?
It’s a big world – one where research interests often transcend subfields and disciplinary affiliations. Graduate students should always feel that they can explore what interdisciplinarity has to offer. Often, interdisciplinarity provides a nice contrast to what you’re used to do. For instance, if you’re used to working alone, perhaps you can join a lab for a while and see what it feels like. Or, if you’re used to working only for academic publications, you can try public outreach and see if you like it. That’s one of the things that I like about academia in the US: compared to France, students have more time and space to engage in some amount of intellectual exploration outside of the specific area they’ll end up specializing in. I’m not minimizing disciplinary pressures and the need to pay dues when preparing for the job market, but I would still say that travelling across topics, methods, and disciplines is a good way to figuring out what you like – and what you don’t like – in research. For instance, from where I stand, there are many centers, labs, and non-profits dedicated to the study of digital technologies and artificial intelligence. Most of these places bring together people from academia, industry, activism, policy, and the media. They tend to work collaboratively; they have internships and RA positions for graduate students. As a graduate student, you can build your expertise and networks by doing this. The research you’ll do in these places probably won’t end up in ASR or AJS, but these are not the only markers of success. In fact, in many interdisciplinary departments, white papers, conference proceedings, and public-facing articles are also relevant signals of expertise and productivity. I’m not saying this kind of collaborative, lab-based, or non-profit model of work is perfect – far from it. But graduate school does not need to be a solitary tunnel where only single-authored publications in top journals count. Doing interesting research is bigger than this, and that’s probably a good thing.