I’ve just left the ReFig 2015 workshop. I kept my workshop notes at Re-Figuring Innovation in Games (ReFig) 2015. Important research with an awesome team that I hope to follow. Last night we were treated to a conversation with Anita Sarkeesian that was fascinating. She has had more of an effect and probably done more good research in preparing the videos than most of the rest of us. There are lessons to be learned about how to address a broader audience, how to have an impact, and how to stay focused on social justice.
Lately I’ve been trying Wolfram Mathematica more an more for analytics. I was introduced to Mathematica by Bill Turkel and Ian Graham who have done some impressive stuff with it. Bill Turkel has now created a open access, open content, and open source textbook Digital Research Methods with Mathematica. The text is a Mathematica notebook itself so, if you have Mathematica you can actually use the text to do analytics on the spot.
Just left a most delightful conference on Key ideas and concepts of Digital Humanities in Darmstadt, Germany. My conference notes are on philosophi.ca : Digital Humanities Concepts 2015. The conference brought together an extraordinary set of speakers who were influential in the field when I entered it. Susan Hockey, Michael Sperberg-McQueen, Nancy Ide, George Landow, Wilhelm Ott and the list goes on. I would be hard pressed to imagine a conference I have been at better able to reflect on the history and ideas of humanities computing. The organizers Andrea Rapp, Michael Sperberg-McQueen, Sabine Bartsch and Michael Bender deserve much more praise than I was able to lavish on them.
Among all the great papers I will mention:
Michael Sperberg-McQueen gave a very smart and well argued paper on descriptive markup arguing against its dismissal as enforcing hierarchies.
Marco Passarotti talked about the Index Thomisticus (which he directs) and the Busa Archive. He brought some documents including some Gantt charts and early letters. I am definitely going to visit him and the archive in Milan.
Fotis Jannidis gave a great paper on topic modelling and its temptations. He has very interesting stuff to say about how the method has been adopted by humanists.
Julia Flanders gave a paper on “Looking for Gender in the History of DH” that when published will, I predict, become mandatory reading. She gives us a way forward after what happened at DH 2015. It was a truly wise and humble talk that could go a long way to providing an inclusive way forward.
Nancy Ide gave a great overview of the separate trajectories taken by DH and Corpus Linguistics.
Peter Robinson gave a call for open editions and walked us through what that might mean.
Given the speakers, there was a lot of reflection on the history of humanities computing and disciplinarity, though enframed by a German context. TU Darmstadt has an MA in Linguistic and Literary Computing (see image of the structure of the degree above) and is now developing an undergrad degree.
Over the last weeks, with lots of help from others, I wrote a position on what is happening in Japan, On Starving the Humanities and Social Sciences of Students and Funding in Japan: 4Humanities’ View. The blog entry presents a 4Humanities view on what appears to be a troubling pattern of de-funding the humanities, arts and social sciences under the mistaken view that they do not contribute to economic growth. The evidence, at least is Canada, is that humanities students do get jobs and do do better than those without university education. They may not do better than those getting a degree in petroleum engineering, but we surely don’t need only engineers. (See my entry on Ignoring the Liberal Arts.)
The obvious weakness of text mining is that it operates on the novel as text, specifically digital text (or string.) We need to find ways to also study the novel as material object (thing), as a social object, as a performance (of the reader), and as an economic object in a market place. Then we also have to find ways to connect these.
So many analytical and mining processes depend on bags of words from dictionaries to topics. Is this a problem or a limitation? Can we try to abstract characters, plot, or argument.
I was interested in the philosophical discussions around the epistemological in novels and philosophical claims about language and literature.
Agencies that fund social science and humanities (SSH) research should move towards requiring a Data Management Plan (DMP) as part of their application processes in cases where research data will be gathered, generated, or curated. In developing policies, funding agencies should consult the community on the values of stewardship and research that would be strengthened by requiring DMPs. Funding agencies should also gather examples and data about reuse of archived data in the social sciences and humanities and encourage due diligence among researchers to make themselves aware of reusable data.
On the surface the recommendation seems rather bland. SSHRC has required the deposit of research data they fund for decades. The problem, however, is that few of us pay attention because it is one more thing to do, and something that shares hard-won data with others that you may want to continue milking for research. What we lack is a culture of thinking of the deposit of research data as a scholarly contribution the way the translation and edition of important cultural texts is. We need a culture of stewardship as a TC3+ (tri-council) document put it. See Capitalizing on Big Data: Toward a Policy Framework for Advancing Digital Scholarship in Canada (PDF).
Given the potential resistance of colleagues it is important that we understand the arguments for requiring planning around data management and that is one of the things we do in this report. Another issue is how to effectively require at the funding proposal end something (like a Data Management Plan) that would show how the researchers are thinking through the issue. To that end we document the approaches of other funding bodies. The point is that this is not actually that new and some research communities are further ahead.
At the end of the day, what we really need is a recognition that depositing data so that it can be used by other researchers is a form of scholarship. Such scholarship can be assessed like any other scholarship. What is the data deposited and what is its quality? How is the data deposited? How is it documented? Can it have an impact?
I blog about this now as I just finished a day-long meeting of the Leadership Council for Digital Infrastructure where we discussed a submission to Industry Canada that calls for coordinated digital research infrastructure. While the situation is different, we need to learn from projects like Bamboo when we imagine massive investment in research infrastructure. We all know it is important, but doing it right is not as easy as it sounds.
Which brings me back to failure. There are three types of failure:
The simple type we are happy to talk about where you ran an experiment based on a hypothesis and didn’t get positive results. This type is based on a simplistic model of the scientific process which pretends to value negative results as much as positive ones. We all know the reality is not that simple and, for that matter, that the science model doesn’t really apply to the humanities.
The messy type where you don’t know why you failed or what exactly failed. This is the type where you promised something in a research or infrastructure proposal and didn’t deliver. This type is harder to report because it reflects badly on you. It is an admission that you were confused or oversold your project.
The third and bitter type is the project that succeeds on its own terms, but is surpassed by the disciplines. It is when you find your research isn’t current any longer and no one is interested in your results. It is when you find yourself ideologically stranded doing something that someone important has declared critically flawed. It is a failure of assumptions, or theory, or positioning and no one wants to hear about this failure, they just want to avoid it.
When people like Willard McCarty and John Unsworth call for a discussion of failure in the digital humanities they describe the first type, but often mean the second. The idea is to describe a form of failure reporting similar to negative results – or to encourage people to describe their failure as simply negative results. What we need, however, is honest description of the second and third types of failure, because those are expensive. To pretend some expensive project that slowly disappeared in missunderstanding was simply an experiment is missing what was at stake. This is doubly true of infrastructure because infrastructure is not supposed to be experimental. No one pays for roads and their maintenance as an experiment to see if people will take the road. You should be sure the road is needed before building.