Digital Humanities Interview

“Even learning very basic programming, even if you’re not very good at it, I think it totally transforms the idea of what you’re doing with that computer, or what a computer should be able to do for you.”

For the past year or so, Dr Michael Radich, Senior Lecturer in VUW’s Religious Studies Programme, has set aside an hour a day to learn Python, a widely-used, high-level computer language. It’s not common for someone in his field (he specialises in the study of Buddhist texts) to delve this far into the world of programming, but it’s the kind of disciplined dedication that’s enabled him to gain a degree in music composition, a PhD from Harvard, and teach himself no less than ten natural languages.

During an hour-and-a-half long discussion over coffee at Milk and Honey, I learn that getting to grips with his first programming language has been one of the most frustrating endeavours of his career, but also one of the most rewarding.

“I was worried the learning curve would be too long and steep. Now I’ve reached that good point where I figure the process is so intrinsically worthwhile that the calculus of input to endpoint has become immaterial.”

In other words, the payoff has proved to be well worth the time invested, even though “it drives me up the wall quite regularly, and I spit the dummy fairly regularly too.”

The reason I’ve pinned him down for an interview is that Michael belongs to a new generation of humanities researchers embracing computational methods and quantitative analysis to challenge assumptions, find new ways to answer time-honoured questions, and to ask new ones.

His story, like many in this space of digital research, begins with a particular problem; in his case it’s how to accurately determine authorship of select parts of the 3000 canonical Chinese Buddhist texts. To get a sense of the scale Michael’s dealing with, the Chinese Buddhist canon is 243 times larger than all the biblical texts combined. Imagine a set of those Encyclopaedia Britannicas you see forlornly collecting dust at your local Sally Army store — you’d need over four of those to match the canon’s 188 million words.

During a kind of cultural ‘arms race’ in the 90s, however, Japan, Korea and Taiwan all competed to be the first to digitise the full Buddhist canon. Researchers in the field now have access to not one but three digitised versions of the entire corpus. Seeing an opportunity to dig deeper, Michael teamed up with local developer Jamie Norrish to create an open-source tool called TACL (Textual Analysis for Corpus Linguistics), which helps sift through millions of Chinese character strings and returns an avalanche of raw data to explore further.

If the Chinese Buddhist canon is the proverbial haystack, think of TACL as a well-calibrated metal detector capable of pointing the way to more needles than anyone realised even existed. But as Michael contends, to fruitfully sort the desired needles from misleading shrapnel takes the disciplinary knowledge of a human expert. TACL allows for increased exploratory power, but Michael’s extensive and hard-earned expertise provides the explanatory power needed to frame this wealth of evidence in a way which maximises impact and minimises misunderstanding.

It’s a great example of the kind of rich analysis and argument made possible when the minds of machine and man work in tandem. As Michael points out, “Computers are just weird collaborators.”

Over the course of our conversation I’m reminded that even when a topic is esoteric and arcane, a researcher’s passion can be infectious, bridging the gap between the minute details of a disciplinary rabbit hole and the general interest of a layperson such as myself. Seemingly curious and critical in equal measures, I love how Michael’s calm, considered manner gives way to a boyish enthusiasm when discussing the subjects that excite him: not only Buddhist texts, but also language learning and educational philosophy.

On the benefits of learning a second language, for example, he opines: “If you don’t know a second language, just learn one. Not having a second language is practically like missing a limb . . . one of the most important lessons [from learning another language] is that things can be done differently — even apparently simple or obvious things can be said differently, or thought differently.”

Keeping an open mind, embracing the uncertainty that comes with true learning, and always remaining up for a new challenge — these are the qualities that characterise the best academic minds, minds like Michael’s that never seem to settle for the status quo. And increasingly, even in the humanities, these minds are turning to computational methods as another way of realising things can be thought — and done — differently.