I am pleased to announce that Dr. Phaedra Daipha, whose first book I wrote about and enjoyed, will be a guest blogger on Installing (Social) Order this month (October, 2016). She is going to be telling us about her recent work in a new post every week or so. Personally, I am excited to learn more about her work about forecasting (weather forecasting, in this case) and especially her re-thinking of decision-making that extends in new directions previous models of “decision science” from the business school crowd, organizational analysis, and organization studies.
Dr. Daipha a cultural sociologist working at the intersection of STS, organization studies, and social theory. Her research agenda centers on the nature, practice, and institutions of knowledge and technology production, with an eye toward understanding the development and transformation of systems of expertise and the emergence of new forms of coordinated action. She has employed a number of methods and data sources to examine such diverse fields of knowledge and technology production as academic sociology, weather forecasting operations, the commercial fishing industry, and medical care.
Despite the diversity of method and empirical focus, however, her work consistently pursues the following substantive themes: decision making in complex sociotechnical systems; visualization and expertise; object-centered sociality; and professional boundary work. She has pursued these topics in a series of papers, culminating with her recent book,
Masters of Uncertainty: Weather Forecasters and the Quest for Ground Truth.
She is currently in the process of completing her forthcoming book, How Doctors Make Decisions: The Role of Prognosis in Cardiology Practice, based on two and a half years of comparative fieldwork. This book builds on her previously developed model of the process of decision making to highlight the practical, materialist, prospective, and situationist character of clinical judgment and care. But it also considerably extends her earlier conceptualization by applying it to a decision-making field that is interventionist (rather than consultative), that relies on cross-functional (rather than single-specialist) teamwork, and that operates within a significantly longer window of uncertainty.
Welcome aboard!
Installing (Social) Order 
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very good thanks and looking forward to the coming posts
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Since you have read my book already, you know that I disagree with the tendency to conceptualize social practices as routinized activity. As I argue (and show), practices may be routine but they are rarely routinized. Humans are not automata and are sooner or later replaced by machines wherever routinized work is called for (even in fast-food chains). Certainly in expert settings, humans are called upon to precisely think outside the box: to use their skills to add value to machine/AI-generated information/solutions. That is why is many such settings one very often encounters what I have called a “culture of disciplined improvisation”…
I don’t want to say much more about this here because that would get us deep into my book, but the point is: even in institutional environments where improvisation is encouraged, such improvisation is disciplined because organizationally regulated. So you get regularities through various socialtechnical disciplining mechanisms (and I will be blogging a bit about this in the following weeks). But also social science (including case studies) is all about identifying, elaborating, and explaining regularities in social life. You might disagree of course, but that’s precisely what I do in my book through the weather forecasting case. Not only do I rigorously identify key aspects of the process of meteorological decision making, but I also trace how they are interrelated in practice and develop a conceptual framework which I then begin to compare against the process of decision making in medicine and finance (based on the extant voluminus literature). And currently I am further exploring these regularities and expanding my conceptual framework for the process of decision making by systematically studying cardiology in action.
I hope this clarifies some things and can serve as a common ground for the discussion in the weeks ahead.
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in some (perhaps?) sense lab-science before STS and after, what if we approach the social sciences via STS?
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sure (and yes to the still open question of to what degree a social-science might be more an ideal (or an oxymoron?) than a reality), but to move on yes won’t be as fine-tuned but with many highly routinized activities one might be able to point to common factors/skills/etc (think how fast-food-chains have done their Fordism thing on jobs like being a cashier or cooking, etc) so can we do something similar when we are looking into more complex (and open-ended, even improvisational) doings? Perhaps there are regularities (or enough regularity to make generalizations as long as we compare them as much, if not more, to our reasons for employing them as we try and test/match them against the in the field happenings) but not logoi out in the world?
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It increasingly sounds like this is a long-running conversation in this blog. I have the feeling I am missing important parts of it that were discussed in the past… Still, it seems to me that what you are describing, dmf, is not only an issue facing case studies/qualitative research but social science research more generally. Surely, one cannot equate human beings/groups with chemical atoms/groups. The processes underlying natural phenomena (even dynamically complex, chaotic systems such as the weather) exhibit universal, predictable patterns in a way that social phenomena will never do. There is an ongoing discussion in social science circles about the predictive value of social science research but, regardless of where one positions oneself in that debate, the fact remains that we will never be able to predict social behavior/phenomena at the same level of accuracy and precision as we make predictions about natural phenomena. Hence the all-important parenthesis in the title of this blog, “Imposing (social) order”, right?
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“project-ing rather than discovering” is a powerful issue at play, and worthy of discussion; however, where does that position the role of observation and intervention? I only think of it today because I was teaching “laboratory studies” and talking about the realist claim that nature is orderly (or, at minimum, contains recognizable patterns that can stabilize) and that when we discover consistencies, orderliness, and laws, then those consistencies, orderliness, and laws pre-existed our observation of it (whether through intervention, transformation, tinkering, etc.); this, then, is compared to the more constructivist apporach to science wehre we impose the orderliness of science in our systematic attempts to inquire about nature by transforming it, messing about with it, and nlearning natural things about the natural world through unnatural means, such as the laboratory, those fruit flies, and the wistar (lab) rat…
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somewhat, I don’t come away from these kinds of case-studies (as pleasingly thick as this one is) with the kinds of understanding I might say from reading about a chemistry experiment where I have some generalizable/universal understanding of what the active powers/potentials are of the components/elements involved and how they interact, my sense is that when it comes to more complex/multivariate/contextualized matters like expertise we are more in the realm of what Wittgenstein framed as familial-resemblances than we are anything like mechanics. Which is in part to suggest that when we treat them more like mechanized/routinize-able assemblages we are inventing and project-ing rather than discovering, and as you all know there is much to be said/made of the work(s) of modeling from STS/ANT perspectives.
In some ways this is along the lines of the debates around structuralism, but to bring it out of the 70’s I think that https://en.wikipedia.org/wiki/Stephen_Park_Turner ‘s work on habits and expertise echo the more meta waxing of Donald Davidson
“I conclude that there is no such thing as a language, not if a language is anything like what many philosophers and linguists have supposed. There is therefore no such thing to be learned, mastered, or born with. We must give up the idea of a clearly defined shared structure which language-users acquire and then apply to cases. And we should try again to say how convention in any important sense is involved in language; or, as I think, we should give up the attempt to illuminate how we communicate by appeal to conventions.”
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Yeah, I tend toward that resource during discussions like this too (small world). At any rate, indeed, I too have noticed that scientific consensus regarding case studies and causality has been building (slowly) over the past couple of decades, even among reluctant scientists or even those that do not consider qualitative analysis to be “scientific” (mainly depending upon the definitions used regarding what knowledge is, concerns over reproducibility of experiments, and so on).
That said, I think that dmf is riffing on a long-running discussion on the blog regarding distinctions between learning/discovering from observational research for the purpose of generating concepts to be used in daily life (or professional life, wherever) and, in contrast, experimenting/intervening in our objects of study (delocalizing them, de-naturalizing them, dissecting them) in order to tease out mechanisms, models, circumstantial variation, etc.
Does that capture what you’re asking about dmf?
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you have some rather long lists of the many factors/aspects of their expertise/expert-practice (and the related complexities of context, feedback, etc) and I wonder how one might tease out the degrees to which any individual has/employs such features ,and to what particular effects, and than how much uniformity/standardization is there from one practitioner to another, also how do we understand how they do or don’t than assemble their particular takes/modes/etc? what sorts of measures might one employ in reverse-engineering any particular assembly/event?
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For a comprehensive and thorough answer to your question, I would direct you to the White Paper of the 2003 NSF Workshop on the “Scientific Foundations of Qualitative Research”. And you might also enjoy the special issue of the American Journal of Sociology [Vol. 119, No. 3 (November 2013)] on “Causal Thinking and Ethnographic Research”.
But to give you my own position on the matter: I believe that, yes, case studies (and qualitative research more generally) do generate and test theory, not only concepts or tools. The last chapter of my book aims to do exactly that. Furthermore, under certain conditions, case studies can make (and have made) rigorous claims about the “how” of causal relations, i.e. the precise mechanisms linking cause and effect. There are more historical case studies that come to mind here, but ethnographic research is increasingly taking on the issue of causality as well.
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Phaedra, want to take a crack at this question/concern?
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that we can generalize; make predictions, point to (even experiment with) systems/mechanisms of causality, etc.
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What does “science-like” imply?
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Thank you for joining us!
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Many thanks for the warm invitation and welcome, Nicholas! I’m thrilled to be here, and I look forward to many a lively conversation in the weeks to come!
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excellent, just finished the forecasting book and looking forward to the coming one, still have my old questions about whether or not these kinds of case-studies lead us to something science-like or rather generate tools/techniques/prototypes to be bricolaged as needed.
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