A broader purpose

The weather prevents me from being there physically, but this is a transcript of my remarks for “Varieties of Digital Humanities,” MLA, Jan 5, 2018.

Using numbers to understand cultural history is often called “cultural analytics”—or sometimes, if we’re talking about literary history in particular, “distant reading.” The practice is older than either name: sociologists, linguists, and adventurous critics like Janice Radway have been using quantitative methods for a long time.

But over the last twenty years, numbers have begun to have a broader impact on literary study, because we’ve learned to use them in a wider range of ways. We no longer just count things that happen to be easily counted (individual words, for instance, or books sold). Instead scholars can start with literary questions that really interest readers, and find ways to model them. Recent projects have cast light, for instance, on the visual impact of poetry, on imagined geography in the novel, on the instability of gender, and on the global diffusion of stream of consciousness. Articles that use numbers are appearing in central disciplinary venues: MLQ, Critical Inquiry, PMLA. Equally important: a new journal called Cultural Analytics has set high standards for transparent and reproducible research.

Of course, scholars still disagree with each other. And that’s part of what makes this field exciting. We aren’t simply piling up facts. New methods are sparking debate about the nature of the knowledge literary historians aim to produce. Are we interpreting the past or explaining it? Can numbers address perspectival questions? The name for these debates is “critical theory.” Twenty years from now, I think it will be clear that questions about quantitative models form an important unit in undergraduate theory courses.

Literary scholars are used to imagining numbers as tools, not as theories. So there’s translation work to be done. But translating between theoretical traditions could be the most important part of this project. Our existing tradition of critical theory teaches students to ask indispensable questions—about power, for instance, and the material basis of ideology. But persuasive answers to those questions will often require a lot of evidence, and the art of extracting meaningful patterns from evidence is taught by a different theoretical tradition, called “statistics.” Students will be best prepared for the twenty-first century if they can connect the two traditions, and do critical theory with numbers.

So in a lot of ways, this is a heady moment. Cultural analytics has historical discoveries, lively theoretical debates, and a public educational purpose. Intellectually, we’re in good shape.

But institutionally, we’re in awful shape. Or to be blunt: we are shape-less. Most literature departments do not teach students how to do this stuff at all. Everything I’ve just discussed may be represented by one unit in one course, where students play with topic models. Reduced to that size, I’m not sure cultural analytics makes any sense. If we were seriously trying to teach students to do critical theory with numbers, we would need to create a sequence of courses that guides them through basic principles (of statistical inference as well as historical interpretation) toward projects where they can pose real questions about the past.

What keeps us from building that curriculum? Part of the obstacle, I think, is the term digital humanities itself. Don’t get me wrong: I’m grateful for the popularity of DH. It has lent energy to many different projects. But the term digital humanities has been popular precisely because it promises that all those projects can still be contained in the humanities. The implicit pitch is something like this: “You won’t need a whole statistics course. Come to our two-hour workshop on topic models instead. You can always find a statistician to collaborate with.”

I understand why digital humanists said that kind of thing eight years ago. We didn’t want to frighten people away. If you write “Learn Another Discipline” on your welcome mat, you may not get many visitors. But a deceptively gentle welcome mat, followed by a trapdoor, is not really more welcoming. So it’s time to be honest about the preparation needed for cultural analytics. Young people entering this field will need to understand the whole process. They won’t even be able to pose meaningful questions, for instance, without some statistics.

Trompe l'oeil door mural

Trompe l’oeil faux door mural from http://www.bumblebee murals.com/cool-wall-murals/

But the metaphor of a welcome mat may be too optimistic. This field doesn’t have a door yet. I mean, there is no curriculum. So of course the field tends to attract people who already have an extracurricular background—which, of course, is not equally distributed. It shouldn’t surprise us that access is a problem when this field only exists as a social network. The point of a classroom is to distribute knowledge in a more equal, less homosocial way. But digital humanities classes, as currently defined, don’t really teach students how to use numbers. (For a bracingly honest exploration of the problem, see Andrew Goldstone.) So it’s almost naive to discuss “barriers to entry.” There is no entrance to this field. What we have is more like a door painted on the wall. But we’re in denial about that—because to admit the problem, we would have to admit that “DH” isn’t working as a gateway to everything it claims to contain.

I think the courses that can really open doors to cultural analytics are found, right now, in the social sciences. That’s why I recently moved half of my teaching to a School of Information Sciences. There, you find a curricular path that covers statistics and programming along with social questions about technology. I don’t think it’s an accident that you also find better gender and ethnic diversity among people using numbers in the social sciences. Methods get distributed more equally within a discipline that actually teaches the methods. So I recommend fusing cultural analytics with social science partly because it immediately makes this field more diverse. I’m not offering that as a sufficient answer to problems of access. I welcome other answers too. But I am suggesting that social-scientific methods are a necessary part of access. We cannot lower barriers to entry by continuing to pretend that cultural analytics is just the humanities, plus some user-friendly digital tools. That amounts to a trompe-l’oeil door.

What the social sciences lack are courses in literary history. And that’s important, because distant readers set out to answer concrete historical questions. So the unfortunate reality is, this project cannot be contained in one discipline.  The questions we try to answer are taught in the humanities. But the methods we use are taught, right now, in the social sciences and data science. Even if it frightens some students off, we have to acknowledge that cultural analytics is a multi-disciplinary project—a bridge between the humanities and quantitative social science, belonging equally to both.

I’m not recommending this approach for the DH community as a whole. DH has succeeded by fitting into the institutional framework of the humanities. DH courses are often pitched to English or History majors, and for many topics, that works brilliantly. But it’s awkward for quantitative courses. To use numbers wisely, students need preparation that an English major doesn’t provide. So increasingly I see the quantitative parts of DH presented as an interdisciplinary program rather than a concentration in the humanities.

dooropenIn saying this, I don’t mean to undersell the value of numbers for humanists. New methods can profoundly transform our view of the human past, and the research is deeply rewarding. So I’m convinced that statistics, and even machine learning, will gradually acquire a place in the humanistic curriculum.

I’m just saying that this is a bigger, slower project than the rhetoric of DH may have led us to expect. Mathematics doesn’t really come packaged in digital tools. Math is a way of thinking, and using it means entering into a long-term relationship with statisticians and social scientists. We are not borrowing tools for private use inside our discipline, but starting a theoretical conversation that should turn us outward, toward new forms of engagement with our colleagues and the world.

What is the point of studying culture with numbers, after all? It’s not to change English departments, but to enrich the way all students think about culture. The questions we’re posing can have real implications for the way students understand their roles in history—for instance, by linking their own cultural experience to century-spanning trends. Even more urgently, these questions give students a way to connect interpretive insights and resonant human details with habits of experimental inquiry.

Instead of imagining cultural analytics as a subfield of DH, I would almost call it an emerging way to integrate the different aspects of a liberal education. People who want to tackle that challenge are going to have to work across departments to some extent: it’s not a project that an English department could contain. But it is nevertheless an important opportunity for literary scholars, since it’s a place where our work becomes central to the broader purposes of the university as a whole.

Digital humanities as a semi-normal thing

Five years ago it was easy to check on new digital subfields of the humanities. Just open Twitter. If a new blog post had dropped, or a magazine had published a fresh denunciation of “digital humanities,” academics would be buzzing.

In 2017, Stanley Fish and Leon Wieseltier are no longer attacking “DH” — and if they did, people might not care. Twitter, unfortunately, has bigger problems to worry about, because the Anglo-American political world has seen some changes for the worse.

But the world of digital humanities, I think, has seen changes for the better. It seems increasingly taken for granted that digital media and computational methods can play a role in the humanities. Perhaps a small role — and a controversial one — and one without much curricular support. But still!

In place of journalistic controversies and flame wars, we are finally getting a broad scholarly conversation about new ideas. Conversations of this kind take time to develop. Many of us will recall Twitter threads from 2013 anxiously wondering whether digital scholarship would ever have an impact on more “mainstream” disciplinary venues. The answer “it just takes time” wasn’t, in 2013, very convincing.

But in fact, it just took time. Quantitative methods and macroscopic evidence, for instance, are now a central subject of debate in literary studies. (Since flame wars may not be entirely over, I should acknowledge that I’m now moving to talk about one small subfield of DH rather than trying to do justice to the whole thing.)

The immediate occasion for this post is a special issue of Genre (v. 50, n. 1) engaging the theme of “data” in relation to the Victorian novel; this follows a special issue of Modern Language Quarterly on “scale and value.” Next year, “Scale” is the theme of the English Institute, and little birds tell me that PMLA is also organizing an issue on related themes. Meanwhile, of course, the new journal Cultural Analytics is providing an open-access home for essays that make computational methods central to their interpretive practice.

The participants in this conversation don’t all identify as digital humanists or distant readers. But they are generally open-minded scholars willing to engage ideas as ideas, whatever their disciplinary origin. Some are still deeply suspicious of numbers, but they are willing to consider both sides of that question. Many recent essays are refreshingly aware that quantitative analysis is itself a mode of interpretation, guided by explicit reflection on interpretive theory. Instead of reifying computation as a “tool” or “skill,” for instance, Robert Mitchell engages the intellectual history of Bayesian statistics in Genre.

Recent essays also seem aware that the history of large-scale quantitative approaches to the literary past didn’t begin and end with Franco Moretti. References to book history and the Annales School mix with citations of Tanya Clement and Andrew Piper. Although I admire Moretti’s work, this expansion of the conversation is welcome and overdue.

If “data” were a theme — like thing theory or the Anthropocene — this play might now have reached its happy ending. Getting literary scholars to talk about a theme is normally enough.

In fact, the play could proceed for several more acts, because “data” is shorthand for a range of interpretive practices that aren’t yet naturalized in the humanities. At most universities, grad students still can’t learn how to do distant reading. So there is no chance at all that distant reading will become the “next big thing” — one of those fashions that sweeps departments of English, changing everyone’s writing in a way that is soon taken for granted. We can stop worrying about that. Adding citations to Geertz and Foucault can be done in a month. But a method that requires years of retraining will never become the next big thing. Maybe, ten years from now, the fraction of humanities faculty who actually use quantitative methods may have risen to 5% — or optimistically, 7%. But even that change would be slow and deeply controversial.

So we might as well enjoy the current situation. The initial wave of utopian promises and enraged jeremiads about “DH” seems to have receded. Scholars have realized that new objects, and methods, of study are here to stay — and that they are in no danger of taking over. Now it’s just a matter of doing the work. That, also, takes time.

Two syllabi: Digital Humanities and Data Science in the Humanities.

When I began teaching graduate courses about digital humanities, I designed syllabi that tried to cover a little of everything.

I enjoyed teaching those courses, but if I’m being honest, it was a challenge to race from digital editing — to maps and networks — to distant reading — to critical reflection on the concept of DH itself. It was even harder to cover that range of topics while giving students meaningful hands-on experience.

The solution, obviously, was to break the subject into more than one course. But I didn’t know how to do that within an English graduate curriculum. Many students are interested in learning about “digital humanities,” because a lot of debate has swirled around that broad rubric. I think the specific fields of inquiry grouped under the rubric actually make better-sized topics for a course, but they don’t have the same kind of name recognition, and courses on those topics don’t enroll as heavily.

This problem became easier to solve when part of my job moved into the School of Information Sciences. Many aspects of digital humanities — from social reflection on information technology to data mining — are already represented in the curriculum here. So I could divide DH into parts, and still have confidence that students would recognize those parts and understand how each part fit into an existing program of study.

This year I’ve taught two courses in the LIS curriculum. I’m sharing syllabi for both at once so I can also describe the contrast between them.

1. The first of the two, “Digital Humanities” (syllabus), is fundamentally a survey of DH as a social phenomenon, with special emphasis on the role of academic libraries and librarians — since that is likely to be a career path that many MLIS students are considering. The course covers a wide range of humanistic themes and topics, but doesn’t go very deeply into hands-on exploration of methods.

2. The second course, “Data Science in the Humanities” (syllabus)  covers the field that digital humanists often call “cultural analytics” — or “distant reading,” when it focuses on literature. Although I know its history is actually more complex, I’m characterizing this field as a form of data science in order to highlight its value for a wide range of students who may or may not intend to work as researchers in universities. I think humanistic questions can be great training for the slippery problems one encounters in business and computational journalism, for instance. But as Dennis Tenen and Andrew Goldstone (among others) have rightly pointed out, it can be a huge challenge to cover all the methods required for this sort of work in a single course. I’m not sure I have a perfect solution to that problem yet. The course is only in its third week! But we are aiming to achieve a kind of hands-on experience that combines Python programming with basic principles of statistics and machine learning, and with reflection on the challenges of social interpretation. I believe this may be achievable, in a course that doesn’t have to cover other aspects of DH, and when many students have at least a little previous experience, both in programming and in the humanities.

As Jupyter notebooks for the data science course are developed, I’m sharing them in a github repo. In both of the syllabi linked above, I also mention other syllabi that served as models. My thanks go out to everyone who shared their experience; I leaned on some of those models very heavily.

data_science_vdThe question I haven’t resolved yet is, How do we connect courses like these to an English curriculum? That connection remains crucial: I chose the phrase “data science” partly because the conversation around data science has explicitly acknowledged the importance of domain expertise. (See Drew Conway’s famous Venn diagram on the right.) I do think researchers need substantive knowledge about specific aspects of cultural history in order to frame meaningful questions about the past and interpret the patterns they find.

Right now, the courses I’m offering in LIS are certainly open to graduate students from humanities departments. But over the long run, I would also like to develop courses located in humanities departments that focus on specific literary-historical problems (for instance, questions of canonicity and popularity in a particular century), integrating distant-reading approaches only as one element of a broader portfolio of methods. Courses like that would fit fairly easily into an English graduate curriculum.

On the other hand, none of the courses I’ve described above can (by themselves) solve the most challenging pedagogical problem in DH, which is to make distant reading useful for doctoral dissertations. Right now, that’s very hard. The research opportunities in distant reading are huge, I believe, but that hugeness becomes itself a barrier. A field where you start making important discoveries after two to three years initial start-up time (training yourself, developing corpora, etc) is not ideally configured for the individualistic model of doctoral research that prevails in the humanities. Collective lab-centered projects are probably a better fit for this field. We may need to envision dissertations as being (at least in part) pieces of a larger research project, exploring one aspect of a shared problem.

Digital humanities might never be evenly distributed.

In an eloquent and pragmatic blog post about building the UCL Centre for Digital Humanities, Melissa Terras stresses the importance of rooting a DH center in local institutional culture, in order to “link people” across the whole spectrum from arts and humanities to computer science and engineering. It’s an impressive achievement that has clearly fostered a lot of significant work at UCL, and it has started to change my own way of thinking about this perplexing phrase “digital humanities.”

In the past, I’ve tended to understand “digital humanities” as an abstract term. If you understand it that way, it’s easy to see that it covers a whole range of disparate things, which has sometimes led me to predict that it would fall apart in the near future into a bunch of separate projects.

Alan Liu, “Map of Digital Humanities” — photo by Quinn Dombrowski at UC Berkeley, August 17, 2015. CC-BY-SA.

But as time passes and the darn thing refuses to fall apart, it seems appropriate to revisit that prediction. I still think digital humanities is hard to define, but apparently, being hard to define doesn’t prevent human institutions from enduring and growing. When I read it a few months ago, Melissa’s post made me reflect that “DH” doesn’t have to be defined abstractly at all. It could be understood, quite concretely, as an institutional achievement that happens to exist on some campuses and not others.

If you understand DH abstractly, as a rubric covering many different projects, there’s a lot of it going on here at UIUC. On the west side of campus, we have a leading school of Library and Information Science (GSLIS), which regularly offers courses on digital humanities, and is one of two institutions piloting HathiTrust Research Center. At the north end of campus, we have the National Center for Supercomputing Applications (NCSA), which excels at providing computational support for the arts, humanities, and social sciences. The Colleges of Media, and Fine and Applied Arts, and Liberal Arts and Sciences are home to a lot of individual scholars pursuing research on or critique of digital media, and the campus as a whole hosts ambitious experiments like Learning to See Systems, that combine technological practice and theory.

On the other hand, we’ve never had a digital humanities center or curricular initiative. We have a program called I-CHASS, at NCSA, which provides computational support to scholars who need it (my own work would have been impossible without their support). And Scholarly Commons, at the Library, helps faculty and students find the resources and training they need. But we don’t have any center of the kind Melissa describes, tasked with building a bridge between all the different people mentioned above, and getting them in the same room.

One way to view this would be: we’re lagging behind. Digital humanities is getting organized at Berkeley and Stanford and Iowa and the University of Pennsylvania and Yale. From time to time I think “we need to get something moving.”

And from time to time I try. But I rapidly discover the size of this campus, and the huge range of digitally-human projects already scattered across it, already moving (quite successfully) in diametrically opposed directions — and it occurs to me, first, that it would take superhuman effort to herd them into the same room, and second, that maybe UIUC doesn’t have a digital humanities center because it doesn’t need one. I’m finding all the resources I need over at GSLIS and NCSA; other kinds of projects are also humming along; maybe we’ve never developed a single center precisely because our various distributed centers are so strong.

There are some drawbacks to this arrangement — mainly, that the strengths of the institution are not well-publicized either internally or abroad. For instance, I’m sure some grad students in the humanities here don’t realize that GSLIS regularly offers excellent courses in digital humanities. I’m writing this blog post partly in hopes of flagging that kind of local opportunity.

I think it’s even harder for undergraduates to envision creative connections between the humanities and other subjects without some kind of interdisciplinary program as a model. This is probably the biggest drawback of our distributed structure, and I do feel I should do something about it. But given the way my own interests fit into the local landscape, I suspect the wheel I can put my shoulder to may be an undergraduate program in data science rather than digital humanities. It seems increasingly possible to me that “digital humanities” — as such — may never take institutional form on this campus. By the time we organize that curricular space, it may be occupied by several distinct projects.

That possibility is making me reflect that public discussion of this topic (as skeptical and wide-ranging as it has been) may still have been too quick to assume we’re all moving in the same direction. William Gibson’s famous quip that the future is already here — but not evenly distributed — encourages us to imagine two possible futures for experiments like digital humanities: either they are destined to (eventually) get distributed everywhere, or they will turn out to have been blind alleys.

I think it’s pretty clear at this point that digital humanities is not a blind alley; there’s too much valuable research and teaching being done under that rubric in too many places, and momentum is continuing to build. But I also doubt its institutions — DH centers and curricula — will ever be evenly distributed. I suspect this is going to be one of those disciplinary spaces that different institutions handle differently even over the long run. In some places, the concept of “DH” may be exactly the seed crystal a local culture needs to bring people together. In other places, institutional DH may fail to coalesce, although — or even because — the interdisciplinary projects it would have organized are separately thriving.

You can’t govern reception.

I’ve read a number of articles lately that posit “digital humanities” as a coherent intellectual movement that makes strong, scary normative claims about the proper future of the humanities as a whole.

Adam Kirsch’s piece in The New Republic is the latest of these; he constructs an opposition between a “minimalist” DH that simply uses computers to edit or read things as we have always done, and a “maximalist” version where technology is taking over English departments and leveling solitary genius in order to impose a cooperative but “post-verbal” vision of the future.

I think there’s a large excluded middle in that picture, where everything interesting actually happens. But I’m resisting — or trying to resist — the urge to write a blog post of clarification and explanation. Increasingly, I believe that’s a futile impulse, not only because “DH” can be an umbrella for many different projects, but more fundamentally because “the meaning of DH” is a perspectival question.

I mean it’s true, objectively, that the number of scholars actually pursuing (say) digital history or game studies is still rather small. But I nevertheless believe that Kirsch is sincere in perceiving them as the narrow end of a terrifying wedge. And there’s no way to prove he’s wrong about that, because threats are very much in the eye of the beholder. Projects don’t have to be explicitly affiliated with each other, or organized around an explicit normative argument, in order to be perceived collectively as an implicit rebuke to some existing scheme of values. In fact, people don’t even really get to choose whether they’re part of a threatening phenomenon. Franco Moretti hasn’t been cheerleading for anything called “digital humanities,” but that point is rapidly becoming moot.

I’m reminded of a piece of advice Mark Seltzer gave me sixteen years ago, during my dissertation defense. Like all grad students in the 90s, I had written an overly-long introduction explaining what my historical research meant in some grander theoretical way. As I recall, he said simply, “you can’t govern your own reception.” A surprisingly hard thing to accept! People of course want to believe that they’re the experts about the meaning of their own actions. But that’s not how social animals work.

So I’m going to try to resist the temptation to debate the meaning of “DH,” which is not in anyone’s control. Instead I’m going to focus on doing cool stuff. Like Alexis Madrigal’s reverse-engineering of Netflix genres, or Mark Sample’s Twitter bots, or the Scholars’ Lab project PRISM, which apparently forgot to take over English departments and took over K-12 education instead. At some future date, historians can decide whether any of that was digital humanities, and if so, what it meant.

(Comments are turned off, because you can’t moderate a comment thread titled “you can’t govern reception.”)

Postscript May 10th: This was written quickly, in the heat of the occasion, and I think my anecdote may be better at conveying a feeling than explaining its underlying logic. Obviously, “you can’t govern reception” cannot mean “never try to change what other people think.” Instead, I mean that “digital humanities” seems to me a historical generalization more than a “field” or a “movement” based on shared premises that could be debated. I see it as closer to “modernism,” for instance, than to “psychology” or “post-structuralism.”

You cannot really write editorials convincing people to like “modernism.” You’d have to write a book. Even then, understandings of the historical phenomenon are going to differ, and some people are going to feel nostalgic for impressionist painting. The analogy to “DH” is admittedly imperfect; DH is an academic phenomenon (mostly! at times it’s hard to distinguish from data journalism), and has slightly more institutional coherence than modernism did. But I’m not sure it has more intellectual coherence.

How much DH can we fit in a literature department?

It’s an open secret that the social phenomenon called “digital humanities” mostly grew outside the curriculum. Library-based programs like Scholars’ Lab at UVA have played an important role; so have “centers” like MITH (Maryland) and CHNM (George Mason) — not to mention the distributed unconference movement called THATCamp, which started at CHNM. At Stanford, the Literary Lab is a sui generis thing, related to departments of literature but not exactly contained inside them.

The list could go on, but I’m not trying to cover everything — just observing that “DH” didn’t begin by embedding itself in the curricula of humanities departments. It went around them, in improvisational and surprisingly successful ways.

That’s a history to be proud of, but I think it’s also setting us up for predictable frustrations at the moment, as disciplines decide to import “DH” and reframe it in disciplinary terms. (“Seeking a scholar of early modern drama, with a specialization in digital humanities …”)

Of course, digital methods do have consequences for existing disciplines; otherwise they wouldn’t be worth the trouble. In my own discipline of literary study, it’s now easy to point to a long sequence of substantive contributions to literary study that use digital methods to make thesis-driven interventions in literary history and even interpretive theory.

But although the research payoff is clear, the marriage between disciplinary and extradisciplinary institutions may not be so easy. I sense that a lot of friction around this topic is founded in a feeling that it ought to be straightforward to integrate new modes of study in disciplinary curricula and career paths. So when this doesn’t go smoothly, we feel there must be some irritating mistake in existing disciplines, or in the project of DH itself. Something needs to be trimmed to fit.

What I want to say is just this: there’s actually no reason this should be easy. Grafting a complex extradisciplinary project onto existing disciplines may not completely work. That’s not because anyone made a mistake.

Consider my home field of literary study. If digital methods were embodied in a critical “approach,” like psychoanalysis, they would be easy to assimilate. We could identify digital “readings” of familiar texts, add an article to every Norton edition, and be done with it. In some cases that actually works, because digital methods do after all change the way we read familiar texts. But DH also tends to raise foundational questions about the way literary scholarship is organized. Sometimes it valorizes things we once considered “mere editing” or “mere finding aids”; sometimes it shifts the scale of literary study, so that courses organized by period and author no longer make a great deal of sense. Disciplines can be willing to welcome new ideas, and yet (understandably) unwilling to undertake this sort of institutional reorganization.

Training is an even bigger problem. People have argued long and fiercely about the amount of digital training actually required to “do DH,” and I’m not going to resolve that question here. I just want to say that there’s a reason for the argument: it’s a thorny problem. In many cases, humanists are now tackling projects that require training not provided in humanities departments. There are a lot of possible fixes for that — we can make tools easier to use, foster collaboration — but none of those fixes solve the whole problem. Not everything can be externalized as a “tool.” Some digital methods are really new forms of interpretation; packaging them in a GUI would create a problematic black box. Collaboration, likewise, may not remove the need for new forms of training. Expecting computer scientists to do all the coding on a project can be like expecting English professors to do all the spelling.

I think these problems can find solutions, but I’m coming to suspect that the solutions will be messy. Humanities curricula may evolve, but I don’t think the majority of English or History departments are going to embrace rapid structural change — for instance, change of the kind that would be required to support graduate programs in distant reading. These disciplines have already spent a hundred years rejecting rapprochement with social science; why would they change course now? English professors may enjoy reading Moretti, but it’s going to be a long time before they add a course on statistical methods to the major.

Meanwhile, there are other players in this space (at least at large universities): iSchools, Linguistics, Departments of Communications, Colleges of Media. Digital methods are being assimilated rapidly in these places. New media, of course, are already part of media studies, and if a department already requires statistics, methods like topic modeling are less of a stretch. It’s quite possible that the distant reading of literary culture will end up being shared between literature departments and (say) Communications. The reluctance of literary studies to become a social science needn’t prevent social scientists from talking about literature.

I’m saying all this because I think there’s a strong tacit narrative in DH that understands extradisciplinary institutions as a wilderness, in which we have wandered that we may reach the promised land of recognition by familiar disciplinary authority. In some ways that’s healthy. It’s good to have work organized by clear research questions (so we aren’t just digitizing aimlessly), and I’m proud that digital methods are making contributions to the core concerns of literary studies.

But I’m also wary of the normative pressures associated with that narrative, because (if you’ll pardon the extended metaphor) I’m not sure this caravan actually fits in the promised land. I suspect that some parts of the sprawling enterprise called “DH” (in fact, some of the parts I enjoy most) won’t be absorbed easily in the curricula of History or English. That problem may be solved differently at different schools; the nice thing about strong extradisciplinary institutions is that they allow us to work together even if the question of disciplinary identity turns out to be complex.

postscript: This whole post should have footnotes to Bethany Nowviskie every time I use the term “extradisciplinary,” and to Matt Kirschenbaum every time I say “DH” with implicit air quotes.

Hold on loosely; or, Gemeinschaft and Gesellschaft on the web.

I want to try a quick experiment.

The digital humanities community must …

If that sounds like a plausible beginning to a sentence, what about this one?

The literary studies community must …

Does that sound as odd to you as it does to me? No one pretends literary studies is a community. In the U.S., the discipline becomes visible to itself mainly at the spectacular, but famously alienating, yearly ritual of the MLA. A hotel that contains disputatious full professors and brilliant underemployed jobseekers may be many interesting things, but “community” is not the first word that comes to mind.

“Digital humanities,” on the other hand, frequently invokes itself as a “community.” The reasons may stretch back into the 90s, and to the early beleaguered history of humanities computing. But the contemporary logic of the term is probably captured by Matt Kirschenbaum, who stresses that the intellectually disparate projects now characterized as DH are unified above all by reliance on social media, especially Twitter.

In many ways that’s a wonderful thing. Twitter is not a perfectly open form, and it’s certainly not an egalitarian one; it has a one-to-many logic. But you don’t have to be a digital utopian to recognize that academic fields benefit from frequent informal contact among their members — what Dan Cohen has described as “the sidewalk life of successful communities.” Twitter is especially useful for establishing networks that cross disciplinary (and professional) boundaries; I’ve learned an amazing amount from those networks.

On the other hand, the illusion of open and infinitely extensible community created by Twitter has some downsides. Ferdinand Tönnies’s distinction between Gemeinschaft and Gesellschaft may not describe all times and places well, but I find it useful here as a set of ideal types. A Gemeinschaft (community) is bound together by personal contact among members and by shared implicit values. It may lack formal institutions, so its members have to be restrained by moral suasion and peer pressure. A Gesellschaft (society) doesn’t expect all its members to share the same values; it expects them to be guided mostly by individual aims, restrained and organized by formal institutions.

Given that choice, wouldn’t everyone prefer to live in cozy Gemeinschaft? Well, sure, except … remember you’re going to have to agree on a set of values! Digital humanists have spent a lot of time discussing values (Lisa Spiro, “Why We Fight”), but as the group gets larger that discussion may prove quite difficult. In the humanities, disagreeing about values is part of our job. It may be just one part of the job in humanities computing, which has a collaborative emphasis. But disagreeing about values has been almost the whole job in more traditional precincts of the humanities. As DH expands, that difference creates yet another layer of disagreement — a meta-struggle over meta-values labeled “hack” and “yack.”

But you know that. Why am I saying all this? I hope the frame I’m offering here is a useful way to understand the growing pains of a web-mediated academic project. DH has at times done a pretty good imitation of Gemeinschaft, but as it gets bigger it’s necessarily going to become more Geselle-y. Which may sound sadder than it is; here’s where I invoke the title of this post. Academic community doesn’t have to be impersonal, but in the immortal words of .38 Special, we need to give each other “a whole lot of space to breathe in.”

This may involve consciously bracketing several values that we celebrate in other contexts. For instance, the centrifugal logic of a growing field isn’t a problem that can be solved by “niceness.” Resolving academic debates by moral suasion on Twitter is not just a bad idea because it produces flame wars. It would be an even worse idea if it worked — because we don’t really want an academic project to have that kind of consensus, enforced by personal ties and displays of collective solidarity.

On the other hand, the values of “candor” and “open debate” may be equally problematic on the web. Filter bubbles have their uses. I want to engage all points of view, but I can’t engage them all at one-hour intervals.

An open question that I can’t answer concerns the role of Twitter here. I’ve found it enormously valuable, both as a latecomer to “DH,” and as an interested lurker in several other fields (machine learning, linguistics, computational social science). I also find it personally enjoyable. But it’s possible that Twitter will just structurally tempt humanists into attempting a more cohesive, coercive kind of Gemeinschaft than academic social networks can (or should) sustain. It’s also possible that we’ll see a kind of cyclic logic here, where Twitter remains valuable for newcomers but tends to become a drain on the time and energy of scholars who already have extensive networks in a field. I don’t know.

Postscript a few hours later: The best reflection on the “cyclic logic” of academic projects online is still Bethany Nowviskie’s “Eternal September of the Digital Humanities,” which remains strikingly timely even after the passage of (gasp) three years.