Blogging in the social, pure, and applied sciences is a common enough practice that two members of the London School of Economics’ Public Policy Group said today that it is “one of the most important things that an academic should be doing right now” — namely, circulating ideas-in-progress to readers in more immediate and (yes) more interesting forms than traditional academic publishing.
It’s no less important in the humanities, even if it’s less common. But in a research field like the digital humanities, blog posts and tweets are the primary way — for many, the only way — that scholars and students disseminate and learn about new questions and methods.
It’s not surprising that digital media would outweigh papers, articles, and books in a field that’s predicated on, well, digital media. But that’s not the only reason that the digital humanities (DH for short) field inhabits this space: it’s also because DH is a field that embraces the openness and provisionality of its research. That is, it openly displays the more tentative, exploratory, maybe-interesting stages of research, the stages when you’re floating queries and fumbling toward conclusions, when you’re testing methods and chasing false leads. In short, DH openly exhibits the work-in-progress stage of all humanities, all academic, research.
Why? Here I’ll suggest two reasons: because we’re working on the kinds of projects that need collaborative solutions (and what’s sometimes called peer-to-peer review), and because the field is changing too fast for the slow pace of traditional academic publishing. These reasons are not definitive, but just my sense of the answer right now, today. And I’ll cite a couple of really good blog posts in the field and say why they helped me formulate these reasons.
Why? Two Reasons
- Most researchers in the DH field are dealing with complex projects, for which their professional (graduate) training didn’t fully prepare them. A common model is the English professor (e.g. me) trained in the literature and culture of a specific historical period (e.g. the early modern era) who now finds him- or herself dealing with computer scientists, digital archivists, computational linguists, textual scholars, and a gallimaufry (I love that word) of other experts. Trying to speak their language is one thing; it’s even harder to stay on top of the latest developments in their fields. For that you need collaborators in those disciplines, and the kind of peer advice that has always turned novices into experts — the kind that journals and other initiatives in DH are fostering.
- Traditionally, scholars would develop ideas in isolation, present interim findings at conferences (talks delivered to other specialists), and publish the results perhaps a year or two later. Now a DH scholar can publish her questions, ideas, and methodology the moment she conjures them up, and distribute the audio file of her talk to a conference or small audience the moment she delivers it. The journal article or book still has an important place in DH research; it’s the final result, the definitive statement (for now), or what Amanda French recently called the “archive” of our ideas. Along the way, though, we can be open about our process, our tools, our questions and methods, and how each of these changed through time. We can be, and we need to be — because the field is shifting under our fingertips. We start investigating a question or an archive or a text, and before long we find new premises or procedures, better tools, more complete archives or more compliant text encoding. The way we find out these things is by following our colleagues on Twitter, by reading their blogs, and posing questions in forums. And writing our own blogs and tweets, calling on the wisdom of the crowd.
Like what? Examples
I’ll now cite a few particular posts that DH scholars have written in the past couple of years. All are by prolific and expert writers in the field. This is not a best-of list; these are just the posts that I’ve bookmarked and returned to a few times — as much for the ways they write and use links and screenshots, as for the insights they offer and questions they raise.
- At the top of my list is Lisa Spiro’s guide to starting out in DH, only because it embodies the field’s openness and low barriers to entry.
- Notice in this post by Matthew Wilkens how he sets out a problem at the beginning, and describes how he’s going to illustrate and then address that problem. His tone is informal but engaging; he uses language you might use in teaching, or explaining something to someone you know well — yet it’s also grammatically correct. He uses both illustrations (screenshots) and subheadings. Unusually, he includes a list of Works Cited at the end; this is characteristic more of a formal article than a blog post. But increasingly in the digital humanities field, that distinction is blurring. If you look at the inaugural issue of Digital Humanities Now, you’ll see that most of its articles are less formal that a journal like Digital Humanities Quarterly. And it publishes audio files as well as written submissions.
- This post by Jonathan Hope uses WordHoard and DocuScope, two text-analysis programs, and a really promising visualization program called LATtice that I want to try. But it’s how Hope uses them that’s really interesting: he combines a number of tools to investigate some queries that actors at Shakespeare’s Globe Theatre posed when he visited them this month, and to consider how he could adapt them for different educational purposes. It’s a model of DH blogging: immediate, multi-pronged, sparked by an observation/query, driven by curiosity, transparent about tools and results, and open-ended.
- Finally, take a look at Matthew Jockers’ post on the limits of Google’s Ngram Viewer, which lets you chart word usage in scanned books between 1800 and 2000. It’s entrancing, but error-prone (all those typos) and opaque (we can’t lift the hood and see which books Google puts in different categories). Jockers concludes with a warning against trusting the data at the expense of digging further: “we must not be seduced by the graphs or by the notion that this data is quantitative and therefore accurate, precise, objective, representative, etc.” Nevertheless, a scholar like Ted Striphas can use it very effectively and subtly.