As a journalist, I can’t wait for tomorrow’s workshop. I’ve heard a lot about ‘distant reading’ (much of it hostile) and while Agate sounds like it’s mainly used for quantitative data, this new JSTOR project looks very promising. I’m particularly interested in how it can help me grasp unfamiliar topics more quickly. I like to write about specialized subjects, and one of the great dangers of that is failing to understand the ‘shape’ and current status of a field because I’m a layperson trying to grasp it very quickly. For instance, I recently had occasion to read a lot of material about Chicana studies, and it took me longer than it should have to get a sense for which writers/concepts are considered to be the most important in the discipline, and which are marginal. (You’d be surprised how rarely information like this is spelled out.) JSTOR’s DfR sounds like it could make those situations a lot easier to avoid.

I found Agate’s homepage a little difficult to understand, so I Googled around for a more accessible explanation of what it’s all about. I found one article that argues Agate’s main utility is as follows:

As journalists, we not only need to solve these problems for practical reporting purposes, but also for philosophical ones. How can we assert that our numbers are correct if we performed a series of manual processes in a spreadsheet exactly once? Do it that way and the only record of how it was done is the one in your head. That’s not good enough. Journalistic integrity requires that we’re able to document and explain our processes.

The idea that multiple iterations are necessary to get accurate data is certainly new to me, although intuitively, it makes sense in processes where probability, chance, and randomness are at play. I’m not quite sure how this applies to us as humanities researchers, since we tend to engage with static datasets more than processes, but I think that’s exactly the developers’ point: providing better tools will enable more process-oriented research methods. What kind of applications do you see for this kind of research going forward?