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Tag: bruno latour

Mathematics and materiality in Latour and Bourdieu’s sociology of science

Our next reading for I School Classics is Pierre Bourdieu’s Science of Science and Reflexivity (2004). In it, rock star sociologist Bourdieu does a sociology of science, but from a perspective of a sociologist who considers himself a scientist. This is a bit of an upset because so much of sociology of science has been dominated by sociologists who draw more from the humanities traditions and whose work undermines the realism the scientific fact. This realism is something Bourdieu aims to preserve while at the same time providing a realistic sociology of science.

Bourdieu’s treatment of other sociologists of science is for the most part respectful. He appears to have difficulty showing respect for Bruno Latour, who he delicately dismisses as having become significant via his rhetorical tactics while making little in the way of a substantive contribution to our understanding of the scientific process.

By saying facts are artificial in the sense of manufactured, Latour and Woolgar intimate that they are fictious, not objective, not authentic. The success of this argument results from the ‘radicality effect’, as Yves Gingras (2000) has put it, generated by the slippage suggested and encouraged by skillful use of ambiguous concepts. The strategy of moving to the limit is one of the privileged devices in pursuit of this effect … but it can lead to positions that are untenable, unsustainable, because they are simply absurd. From this comes a typical strategy, that of advancing a very radical position (of the type: scientific fact is a construction or — slippage — a fabrication, and therefore an artefact, a fiction) before beating a retreat, in the face of criticism, back to banalities, that is, to the more ordinary face of ambiguous notions like ‘construction’, etc.

In the contemporary blogosphere this critique has resurfaced through Nicholas Shackel under the name “Motte and Bailey Doctrine” [1, 2], after the Motte and Bailey castle.

A Motte and Bailey castle is a medieval system of defence in which a stone tower on a mound (the Motte) is surrounded by an area of pleasantly habitable land (the Bailey), which in turn is encompassed by some sort of a barrier, such as a ditch. Being dark and dank, the Motte is not a habitation of choice. The only reason for its existence is the desirability of the Bailey, which the combination of the Motte and ditch makes relatively easy to retain despite attack by marauders. When only lightly pressed, the ditch makes small numbers of attackers easy to defeat as they struggle across it: when heavily pressed the ditch is not defensible, and so neither is the Bailey. Rather, one retreats to the insalubrious but defensible, perhaps impregnable, Motte. Eventually the marauders give up, when one is well placed to reoccupy desirable land.

In the metaphor, the Bailey here is the radical antirealist scientific position wherein facts are fiction, the Motte is the banal recognition that science is a social process. Schackel writes that “Diagnosis of a philosophical doctrine as being a Motte and Bailey Doctrine is invariably fatal.” While this might be true in the world of philosophical scrutiny, this is unfortunately not sociologically correct. Academic traditions die hard, even long after the luminaries who started them have changed their minds.

Latour has repudiated his own radical position in “Why Has Critique Run out of Steam? From Matters of Fact to Matter of Concern” (2004), his “Tarde’s idea of quantification” (2010) offers an insightful look into the potential of quantified sociology when we have rich qualitative data sets that show us the inner connectivity of the societies. Late Latour is bullish about the role of quantification in sociology, though he believes it may require a different use of statistics than has been used traditionally in the natural sciences. Recently developed algorithmic methods for understanding network data prove this point in practice. Late Latour has more or less come around to “Big Data” scientific consensus on the matter.

This doesn’t stop Latour from being used rather differently. Consider boyd and Crawford’s “Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon” (2012), and its use of this very paper of Latour:

‘Numbers, numbers, numbers,’ writes Latour (2010). ‘Sociology has been obsessed by the goal of becoming a quantitative science.’ Sociology has never reached this goal, in Latour’s view, because of where it draws the line between what is and is not quantifiable knowledge in the social domain.

Big Data offers the humanistic disciplines a new way to claim the status of quantitative science and objective method. It makes many more social spaces quantifiable. In reality, working with Big Data is still subjective, and what it quantifies does not necessarily have a closer claim on objective truth – particularly when considering messages from social media sites. But there remains a mistaken belief that qualitative researchers are in the business of interpreting stories and quantitative researchers are in the business of producing facts. In this way, Big Data risks reinscribing established divisions in the long running debates about scientific method and the legitimacy of social science and humanistic inquiry.

While Latour (2010) is arguing for a richly quantified sociology and has moved away from his anti-realist position about scientific results, boyd and Crawford fall back into the same confusing trap set by earlier Latour of denying scientific fact because it is based on interpretation. boyd and Crawford have indeed composed their “provocations” effectively, deploying ambiguous language that can be interpreted as a broad claim that quantitative and humanistic qualitative methods are equivalent in their level of subjectivity, but defended as the banality that there are elements of interpretation in Big Data practice.

Bourdieu’s sociology of science provides a way out of this quagmire by using his concept of the field to illuminate the scientific process. Fields are a way of understanding social structure: they define social positions or roles in terms of their power relations as they create and appropriate different forms of capital (economic, social, etc.) His main insight which he positions above Latour’s is that while a sociological investigation of lab conditions will reveal myriad interpretations, controversies, and farces that may convince the Latourian that the scientists produce fictions, an understanding of the global field of science, with its capital and incentives, will show how it produces realistic, factual results. So Bourdeiu might have answered boyd and Crawford by saying that the differences in legitimacy between quantitative science and qualitative humanism have more to do with the power relations that govern them in their totality than in the local particulars of the social interactions of which they are composed.

In conversation with a colleague who admitted to feeling disciplinary pressure to cite Latour despite his theoretical uselessness to her, I was asked whether Bourdieu has a comparable theory of materiality to Latour’s. This is a great question, since it’s Latour’s materialism that makes him so popular in Science and Technology Studies. The best representation I’ve seen of Bourdieu’s materiality so far is this passage:

“The ‘art’ of the scientist is indeed separated from the ‘art’ of the artist by two major differences: on the one hand, the importance of formalized knowledge which is mastered in the practical state, owing in particular to formalization and formularization, and on the other hand the role of the instruments, which, as Bachelard put it, are formalized knowledge turned into things. In other words, the twenty-year-old mathematician can have twenty centuries of mathematics in his mind because formalization makes it possible to acquire accumulated products of non-automatic inventions, in the form of logical automatisms that have become practical automatisms.

The same is true as regards instruments: to perform a ‘manipulation’, one uses instruments that are themselves scientific conceptions condensed and objectivated in equipment functioning as a system of constraints, and the practical mastery that Polanyi refers to is made possible by an incorporation of the constraints of the instrument so perfect that one is corporeally bound up with it, one responds to its expectations; it is the instrument that leads. One has to have incorporated much theory and many practical routines to be able to fulfil the demands of the cyclotron.”

I want to go so far as to say that in these two paragraphs we have the entire crux of the debate about scientific (and especially data scientific) method and its relationship to qualitative humanism (which Bourdieu would perhaps consider an ‘art’.) For here we see that what distinguishes the sciences is not merely that they quantify their object (Bourdieu does not use the term ‘quantification’ here at all), but rather because it revolves around cumulative mathematical formalism which guides both practice and instrument design. The scientific field aims towards this formalization because that creates knowledge as a capital that can be transferred efficiently to new scientists, enabling new discoveries. In many ways this is a familiar story from economics: labor condenses into capital, which provides new opportunities for labor.

The simple and realistic view that formal, technical knowledge is a kind of capital explains many of the phenomena we see today around data science in industry and education. It also explains the pervasiveness of the humanistic critique of science as merely another kind of humanism: because it is an advertising campaign to devalue technical capital and promote alternative forms of capital associated with the humanities as an alternative. The Bailey of desirable land is intellectual authority in an increasingly technocratic society; the Motte is banal observation of social activity.

This is not to say that the cultural capital of the humanities is not valuable in its own right. However, it does raise questions about the role of habitus in determining taste for the knowledge as art, a topic discussed in depth in Bourdieu’s Distinction. My own view is that while there is a strong temptation towards an intellectual factionalism, especially in light of the unequal distribution of capital (of various kinds) in society, this is ultimately a pernicious trend. I would prefer a united field.

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digital qualities: some meditations on methodology

Text is a kind of data that is both qualitative (interpretable for the qualities it conveys) and qualitative (characterized by certain amounts of certain abstract tokens arranged in a specific order).

Statistical learning techniques are able to extract qualitative distinctions from quantitative data, through clustering processes for example. Non-parametric statistical methods allow qualitative distinctions to be extracted from quantitative data without specifying particular structure or features up front.

Many cognitive scientists and computational neuroscientists believe that this is more or less how perception works. The neurons in our eyes (for example) provide a certain kind of data to downstream neurons, which activate according to quantifiable regularities in neuron activation. A qualitative difference that we perceive is due to a statistical aggregation of these inputs in the context of a prior, physically definite, field of neural connectivity.

A source of debate in the social sciences is the relationship between qualitative and quantitative research methods. As heirs to the methods of harder sciences whose success is indubitable, quantitative research is often assumed to be credible up to the profound limits of its method. A significant amount of ink has been spilled distinguishing qualitative research from quantitative research and justifying it in the face of skeptical quantitative types.

Qualitative researchers, as a rule, work with text. This is trivially true due to the fact that a limiting condition of qualitative research appears to be the creation of a document explicating the research conclusions. But if we are to believe several instructional manuals on qualitative research, then the work of an e.g. ethnographer involves jottings, field notes, interview transcripts, media transcripts, coding of notes, axial coding of notes, theoretical coding of notes, or, more broadly, the noting of narratives (often written down), the interpreting of text, a hermeneutic exposition of hermeneutic expositions ad infinitum down an endless semiotic staircase.

Computer assisted qualitative data analysis software passes the Wikipedia test for “does it exist”.

Data processed by computers is necessarily quantitative. Hence, qualitative data is necessarily quantitative. This is unsurprising, since so much qualitative data is text. (See above).

We might ask: what makes the work qualitative researchers do qualitative as opposed to quantitative, if the data they work with with quantitative? We could answer: it’s their conclusions that are qualitative.

But so are the conclusions of a quantitative researcher. A hypothesis is, generally speaking, a qualitative assessment, that is then operationalized into a prediction whose correspondence with data can be captured quantitatively through a statistical model. The statistical apparatus is meant to guide our expectations of the generalizability of results.

Maybe the qualitative researcher isn’t trying to get generalized results. Maybe they are just reporting a specific instance. Maybe generalizations are up to the individual interpreter. Maybe social scientific research can only apply and elaborate on an ideal type, tell a good story. All further insight is beyond the purview of the social sciences.

Hey, I don’t mean to be insensitive about this, but I’ve got two practical considerations: first, do you expect anyone to pay you for research that is literally ungeneralizable? That has no predictive or informative impact on the future? Second, if you believe that, aren’t you basically giving up all ground on social prediction to economists? Do you really want that?

Then there’s the mixed methods researcher. Or, the researcher who in principle admits that mixed methods are possible. Sure, the quantitative folks are cool. We’d just rather be interviewing people because we don’t like math.

That’s alright. Math isn’t for everybody. It would be nice if computers did it for us. (See above)

What some people say is: qualitative research generates hypotheses, quantitative research tests hypotheses.

Listen: that is totally buying into the hegemony of quantitative methods by relegating qualitative methods to an auxiliary role with no authority.

Let’s accept that hegemony as an assumption for a second, just to see where it goes. All authority comes from a quantitatively supported judgment. This includes the assessment of the qualitative researcher.

We might ask, “Where are the missing scientists?” about qualitative research, if it is to have any authority at all, even in its auxiliary role.

What would Bruno Latour do?

We could locate the missing scientists in the technological artifacts that qualitative researchers engage with. The missing scientists may lie within the computer assisted qualitative data analysis software, which dutifully treats qualitative data as numbers, and tests the data experimentally and in a controlled way. The user interface is the software’s experimental instrument, through which it elicits “qualitative” judgments from its users. Of course, to the software, the qualitative judgments are quantitative data about the cognitive systems of the software’s users, black boxes that nevertheless have a mysterious regularity to them. The better the coding of the qualitative data, the better the mysteries of the black box users are consolidated into regularities. From the perspective of the computer assisted qualitative data analysis software, the whole world, including its users, is quantitative. By delegating quantitative effort to this software, we conserve the total mass of science in the universe. The missing mass is in the software. Or, maybe, in the visual system of the qualitative researchers, which performs non-parametric statistical inference on the available sensory data as delivered by photo-transmitters in the eye.

I’m sorry. I have to stop. Did you enjoy that? Did my Latourian analysis convince you of the primacy or at least irreducibility of the quantitative element within the social sciences?

I have a confession. Everything I’ve ever read by Latour smells like bullshit to me. If writing that here and now means I will never be employed in a university, then may God have mercy on the soul of academe, because its mind is rotten and its body dissolute. He is obviously a brilliant man but as far as I can tell nothing he writes is true. That said, if you are inclined to disagree, I challenge you to refute my Latourian analysis above, else weep before the might of quantification, which will forever dominate the process of inquiry, if not in man, then in our robot overlords and the unconscious neurological processes that prefigure them.

This is all absurd, of course. Simultaneously accepting the hegemony of quantitative methods
and Latourian analysis has provided us with a reductio ad absurdum that compels us to negate some assumptions. If we discard Latourian analysis, then our quantitative “hegemony” dissolves as more and more quantitative work is performed by unthinking technology. All research becomes qualitative, a scholarly consideration of the poetry outputted by our software and instruments.

Nope, that’s not it either. Because somebody is building that software and those instruments, and that requires generalizability of knowledge, which so far qualitative methods have given up a precise claim to.

I’m going to skip some steps and cut to the chase:

I think the quantitative/qualitative distinction in social scientific research, and in research in general, is dumb.

I think researchers should recognize the fungibility of quantity and quality in text and other kinds of data. I think ethnographers and statistical learning theorists should warmly embrace each other and experience the bliss that is finding ones complement.

Goodnight.

Another rant about academia and open source

A few weeks ago I went to a great talk by Victoria Stodden about how there’s a crisis of confidence in scientific research that depends on heavy computing. Long story short, because the data and code aren’t openly available, the results aren’t reproducible. That means there’s no check on prior research, and bad results can slip through and be the foundation for future work. This is bad.

Stodden’s solution was to push forward within the scientific community and possibly in legislation (i.e., as a requirement on state-funded research) for open data and code in research. Right on!

Then, something intriguing: somebody in the audience asked how this relates to open source development. Stodden, who just couldn’t stop saying amazing things that needed to be said that day, answered by saying that scientists have a lot to learn from the “open source world”, because they know how to build strong communities around their (open) work.

Looking around the room at this point, I saw several scientists toying with their laptops. I don’t think they were listening.

It’s a difficult thing coming from an open source background and entering academia, because the norms are close, but off.

The other day I wrote in an informal departmental mailing list a criticism and questions about a theorist with a lot of influence in the department, Bruno Latour. There were a lot of reactions to that thread that ranged pretty much all across the board, but one of the surprising reactions I got was along the lines of “I’m not going to do your work for you by answering your question about Latour.” In other words, RTFM. Except, in this case, “the manual” was a book or two of dense academic literature in a field that I was just beginning to dip into.

I don’t want to make too much of this response, since there were a lot of extenuating circumstances, but it did strike me as an indication of one of the cultural divides between open source development and academic scholarship. In the former, you want as many people as possible to understand and use your cool new thing because that enriches your community and makes your feel better about your contribution to the world. For some kinds of scholars, being the only one who understands a thing is a kind of distinction that gives you pride and job opportunities, so you don’t really want other people to know as much as you about it.

Similarly for computationally heavy sciences: if you think your job is to get grants to fund your research, you don’t really want anybody picking through it and telling you your methodology was busted. In an Internet Security course this semester, I’ve had the pleasure of reading John McHugh’s Testing Intrusion Detection Systems: A Critique of the 1998 and 1999 DARPA Off-line Intrusion Detection System Evaluation as Performed by Lincoln Laboratory. In this incredible paper, McHugh explains why a particular DARPA-funded Lincoln Labs Intrusion Detection research paper is BS, scientifically speaking.

In open source development, we would call McHugh’s paper a bug report. We would say, “McHugh is a great user of our research because he went through and tested for all these bugs, and even has recommendations about how to fix them. This is fantastic! The next release is going to be great.”

In the world of security research, Lincoln Labs complained to the publisher and got the article pulled.

Ok, so security research is a new field with a lot of tough phenomena to deal with and not a ton of time to read up on 300 years of epistemology, philosophy of science, statistical learning theory, or each others’ methodological critiques. I’m not faulting the research community at all. However, it does show some of the trouble that happens in a field that is born out of industry and military funding concerns without the pretensions or emphasis on reproducible truth-discovery that you get in, say, physics.

All of this, it so happens, is what Lyotard describes in his monograph, The Postmodern Condition (1979). Lyotard argues that because of cybernetics and information technologies, because of Wittgenstein, because of the “collapse of metanarratives” that would make anybody believe in anything silly like “truth”, there’s nothing left to legitimize knowledge except Winning.

You can win in two ways: you can research something that helps somebody beat somebody else up or consume more, so that they give you funding. Or you can win by not losing, by pulling some wild theoretical stunt that puts you out of range of everybody else so that they can’t come after you. You become good at critiquing things in ways that sound smart, and tell people who disagree with you that they haven’t read your cannon. You hope that if they call your bluff and read it, they will be so converted by the experience that they will leave you alone.

Some, but certainly not all, of academia seems like this. You can still find people around who believe in epistemic standards: rational deduction, dialectical critique resolving to a consensus, sound statistical induction. Often people will see these as just a kind of meta-methodology in service to a purely pragmatic ideal of something that works well or looks pretty or makes you think in a new way, but that in itself isn’t so bad. Not everybody should be anal about methodology.

But these standards are in tension with the day to day of things, because almost nobody really believes that they are after true ideas any more. It’s so easy to be cynical or territorial.

What seems to be missing is a sense of common purpose in academic work. Maybe it’s the publication incentive structure, maybe it’s because academia is an ideological proxy for class or sex warfare, maybe it’s because of a lot of big egos, maybe it’s the collapse of meta-narratives.

In FOSS development, there’s a secret ethic that’s not particularly well articulated by either the Free Software Movement or the Open Source Initiative, but which I believe is shared by a lot of developers. It goes something like this:

I’m going to try to build a totally great new thing. It’s going to be a lot of work, but it will be worth it because it’s going to be so useful and cool. Gosh, it would be helpful if other people worked on it with me, because this is a lonely pursuit and having others work with me will help me know I’m not chasing after a windmill. If somebody wants to work on it with me, I’m going to try hard to give them what they need to work on it. But hell, even if somebody tells me they used it and found six problems in it, that’s motivating; that gives me something to strive for. It means I have (or had) a user. Users are awesome; they make my heart swell with pride. Also, bonus, having lots of users means people want to pay me for services or hire me or let me give talks. But it’s not like I’m trying to keep others out of this game, because there is just so much that I wish we could build and not enough time! Come on! Let’s build the future together!

I think this is the sort of ethic that leads to the kind of community building that Stodden was talking about. It requires a leap of faith: that your generosity will pay off and that the world won’t run out of problems to be solved. It requires self-confidence because you have to believe that you have something (even something small) to offer that will make you a respected part of an open community without walls to shelter you from criticism. But this ethic is the relentlessly spreading meme of the 21st century and it’s probably going to be victorious by the start of the 22nd. So if we want our academic work to have staying power we better get on this wagon early so we can benefit from the centrality effects in the growing openly collaborative academic network.

I heard David Weinberger give a talk last year on his new book Too Big to Know, in which he argued that “the next Darwin” was going to be actively involved in social media as a research methodology. Tracing their research notes will involve an examination of their inbox and facebook feed to see what conversations were happening, because just so much knowledge transfer is happening socially and digitally and it’s faster and more contextual than somebody spending a weekend alone reading books in a library. He’s right, except maybe for one thing, which is that this digital dialectic (or pluralectic) implies that “the next Darwin” isn’t just one dude, Darwin, with his own ‘-ism’ and pernicious Social adherents. Rather, it means that the next great theory of the origin of species is going to be built by a massive collaborative effort in which lots of people will take an active part. The historical record will show their contributions not just with the clumsy granularity of conference publications and citations, but with minute granularity of thousands of traced conversations. The theory itself will probably be too complicated for any one person to understand, but that’s OK, because it will be well architected and there will be plenty of domain experts to go to if anyone has problems with any particular part of it. And it will be growing all the time and maybe competing with a few other theories. For a while people might have to dual boot their brains until somebody figures out how to virtualize Foucauldean Quantum Mechanics on a Organic Data Splicing ideological platform, but one day some crazy scholar-hacker will find a way.

“Cool!” they will say, throwing a few bucks towards the Kickstarter project for a musical instrument that plays to the tune of the uncollapsed probabilistic power dynamics playing out between our collated heartbeats.

Does that future sound good? Good. Because it’s already starting. It’s just an evolution of the way things have always been, and I’m pretty sure based on what I’ve been hearing that it’s a way of doing things that’s picking of steam. It’s just not “normal” yet. Generation gap, maybe. That’s cool. At the rate things are changing, it will be here before you know it.