(This post continues from Part 2, “Process, process, process”)
MAMM team dealing with messy…
Ebru: As the person who built the tools – what are the things that stand out for you when you look back at them now?
Neta: It’s interesting to see how even in the worklog, you can still see personality as well as how the the comfort level of the RAs developed. Their early logs are very short, very hesitant.
E: Very formal?
N: Yes! And the the work is fine, but as the work logs developed, there’s much more dialogue, depth, detail, confidence, but also a willingness to say “I don’t understand this. Do you?”
E: Did you see disciplinary boundaries? Did you see hints of disciplinarity without seeing the names?
N: Oh, yes – there’s our literary studies students who have a confidence with textual details and that vocabulary, but when they had to talk about sense of place, or even latitude and longitude, they would really agonize.
E: Whereas our DH student agonized about everything but has a way of working which is so serious and so welcoming of advice.
N: I think that comes from digital humanities, where group projects are the norm. You cannot do a DH project on your own.
E: And so the idea of taking on advice or asking questions, it’s just normal.
N: Whereas for literary studies students, it’s totally not normal.
E: And for the geography student – she was hesitant about this object and feeling that she wasn’t allowed to take a novel apart –
N: what Martin Eve talks about In Digital Humanities and Literary Studies as background labour. The labour of noticing things, marking them down, making decisions about what you’re seeing. We wanted to make clear that work is analytical, it is interpretive, that work has value and can be done across disciplines in collaboration, with the spirit of generosity and criticism. You can produce a useful integrated reading, even if getting there is a little bit messy.
E: That’s OK. Messy is good. Research is messy.
N: Research is messy and making that messiness visible and learning from it, and sort of rejoicing in it –
E: that’s what WhatsApp is for – rejoicing in the messiness.
Let me frame it this way: in what ways did the collaboration become challenging? When you have people coming from different ontologies, epistemologies – when did it become difficult?
N: It’s hard when you don’t have the same language –
E: and because we didn’t give them instructions – “this is how you should be doing it” – because we were also trying to figure it out because everything was so new.
N: It’s almost like we are creating our own language, but I think what was even harder was not having a shared notion of what it is we’re supposed to be doing.
E: That’s methodology, right? Like, “I think we’re doing this, but we won’t know until we’ve done it.”
N: And the first time we did the data collection, it didn’t work.
E: So, what happened?
N: We had a meeting after they’d gone through their first round of data collection – which didn’t take that long, and it’s an 800-page novel. It took them three weeks to get through it, all five of them working asynchronously, and at the end of it, we had a team meeting and they were open about what worked and what did not work.
But importantly before that meeting, I said, “OK, we’re going to log what the problem is, and figure out our solution.” The DH student was much more comfortable with that because that’s what they do all the time.
E: Yes, in DH debugging is normal. For the other four, the goal is perfection. They are used to just sending something in and then getting the feedback and it’s done.
N: Yes, so I think that our being interested in problems and solutions was the most important turning point and the work became so much more joyful and sophisticated –
E: and they became more invested. Because they saw all of us say, “Oh, this didn’t work! Let’s figure out why.”
So, what did we change?
N: We fixed the categories, updated the glossary because some of our language wasn’t working and we improved it. We moved around some of the categories on the Excel sheet so that the data collection became more logical –
E: and fluid.
N: We added the idea of the second encoder, so now we had an explicit way to ask someone to look at the analytical work again.
E: Yes, and that’s also when we came up with the colour-coding. And we started using WhatsApp more. We started making better use of our informal spaces, and I was going to say: I think the problem wasn’t that the tools weren’t working. Instead, because they’re so trained to work by themselves, they weren’t communicating with each other. And maybe there’s this kind of a fear because there’s so many unknowns.
N: Absolutely.
E: So, the tools improved, but also communication.
N: The communication improved, and I think the rigor of the analysis improved. And it’s not just that it was more fun –
E: It was also better work.
N: And faster!
E: Which is very interesting –
N: They managed to get through two new rounds of data collection – we re-did Oskar and Henry, and then we did a whole new round on Colleen Pelegrim-Froelich. They were able to do that so quickly. And for me as a literary studies person, I can now say, “You can do this reading – you can use this process to think about a character’s spatial arc in a book, and here are all these tools to help you focus on this very, particular interesting thing.” And it works.
E: Yep.
N: So, that was phase one.
E: And it took – in December, we gave them the books and they were reading during the break, right? So that’s only three months.
N: They have done great work. And then we all took April off –
E: because we had exams and marking, and we were all tired and now we’re back.
N: We’re kind of in a new phase.
E: So, you know, till next time.