On day three I only made two sessions – and the second was incredibly disappointing (I have serious problems with the study design) so I’ll just briefly chat about the first, which was pretty awesome.
Monitoring, Modeling, and Memory (II): Methods for the Study of Cyberinfrastructure (and Other Large Distributed Phenomena)
This is a pretty big project that stretches over maybe about 10 schools. Christine Borgman is a/the lead for it. David Ribes talked about his work with a large hydrology project. It was interesting how the technical support folks had to negotiate the needs of both engineers and scientists who had overlapping information sources but really had different goals fundamentally (typical engineering/science split). David Fearon talked about how this distributed qualitative work is managed using nVivo (ew!). I’ve run across Andrea Wiggins’ stuff before. She’s using Taverna and myExperiment to manage cleaning and analysis of social science data on how free/open source software communities work. I guess she’s also helping manage datasets and providing DOIs. S.L. Star talked in pretty abstract terms about how standards outsource morality. hm. There are value choices that are made in the development of standards, and then the standards are applied without explicitly revisiting these choices, I guess.
Kerk Kee’s stuff was quite different – he’s really pulling from a different literature: organizational communication. So his dissertation is on the development of these cyberinfrastructure projects and how structure develops through communication. Seems like the literature he cites is quite distinct from what the other folks cite. His work and David Ribes’ both point to some of the issues the computer scientists have. They must do what counts as research for their own career, they must do requirements engineering for multiple distinct and competing paradigms, and they must learn enough about the area of science to be able to talk to the other parties. Reminds me of some of what I read in Hine, C. (2006). Databases as scientific instruments and their role in the ordering of scientific work. Social Studies of Science, 36(2), 269-298. DOI:10.1177/0306312706054047 (see discussion here)
Some questions from the audience were even more interesting. One person complained that they need to go back to the tradition of ethnography by going and immersing yourself in a strange culture for months or a year before understanding it. The problem, as the panel answered, is that there is no “there” in physical space. There is no physical place to go in order to immerse yourself and find the culture. That’s why there are lots of site visits, and meeting attendance, but there are also reviews of documents and online traces.
Another person asked about the role of the researcher and what if any quid pro quo can be offered? One panel member said that his participants/informants sort of had strange expectations of him: he was supposed to be telling them about their culture and be the representative social scientist. He thought that was weird, because he didn’t really feel like it was his place. Borgman described lots of ways the information scientists on her team are actually contributing to the science projects by developing data sharing policies and helping to organize and provide access to project data and information. (Of course, folks in the audience without a LIS background might not be able to provide that same service)
I’m glad I went to the conference. It was nice to see some of these famous people in person and also to get some fabulous feedback on my presentation. It’s in Tokyo next year so I don’t plan to go.