Of course, I didn't go in expecting that we would somehow "make it all clear" over the course of a 2-hour conversation. Nevertheless, I hoped that we might develop some researchable questions--that even if there was no way to verify them, we might generate some hypotheses about academic hiring that could be tested, given enough time, energy, and data. I think we accomplished this, so I was pretty pleased.

Anyhow, it's an awfully complicated phenomenon to try and diagnose. Basically, we were asking whether or not it's possible to identify trends and patterns in hiring across our disciplinary network. We tried to isolate internal factors that lead to the decision, the kinds of externalities that Watts describes in Six Degrees, and then we talked about the various data available to us to quantify some of these things.

Easy, right?

Umm, no. I think that most people approach hiring from their local place in the network, either as a member of a committee or department that's hiring, or (obviously) as someone who's applying for positions. And that makes it doubly tough to spot trends at a disciplinary level, because our tendency is to project that experience. If we as a department are looking for a visual rhetoric specialist, then folk in the department are going to see that as a representative trend. And all of the local concerns (who's retired or left, where do you want your curricula to go, etc.) seem larger than they actually may be.

Externalities don't make it any easier, because it's tough to know how important a role they play. For example, the college or university may be looking to make particular statements and expect hires to fit in with those. A special issue of a journal or a conversation on a listserv may spark a committee chair's curiosity and interest. And so forth.

I think, ultimately, then, the most productive portion of the conversation came when we started thinking about what sorts of data exist. For me, perhaps the single most valuable source is the MLA Job Information List. Every year, people count the number of jobs, and whether it's gone up or down, but there's a great deal more in there that goes largely untapped. For our purposes, the prospect of taking 5-10 years worth of JILs and doing a basic keyword extraction seemed promising. And translating it into a graphic (like the NYT one from the DN and RN Conventions) might give us some sense of what specialties are emerging, which are withering, etc.

Armed with that information, I think we felt like we could really start generating some knowledge. Basically, our questions would then become questions of triggers or tipping points. Are there specific events in the field, or particular factors, that we could similarly chart and find points of resonance?

So, for example, every year at CCCC, that year's Chair delivers an Address to a fairly sizable portion of the membership. That Address is often published on the website, and eventually in the flagship journal. Given information about the waxing and waning of specializations, we could look at that data alongside the themes of the CCCC from the prior year and/or the Chair's address. If the Chair emphasizes technology in his or her talk, is there a corresponding bump the following year in positions that include technology as one of their areas?

Or, to take a different tack, we could mine the JIL data even further, and try to determine if there are any patterns that emerge within particular specializations. Where are the "cutting-edge" specialties first mentioned? Are there particular schools who, when they hire for a "new" specialist, trigger a cascade across the disciplinary network such that other schools will follow their lead?

Are there ways of controlling for specific regional needs (such as ESL and/or Basic Writing), say, in Texas and California? Can we isolate out different institutional data sets (community colleges on one hand, doctoral programs on the other, liberal arts schools on the third)?

The deeper you dig into a topic like this, the more complicated it gets. But I think that we accomplished a couple of things yesterday. First, we did a good job, I think, with the vocabulary. And second, I think we started to generate some concrete discussion of how "network thinking" (whatever that might be) might help us get some kind of grasp on a pretty intractable problem.

And as always, this recap is filtered through my own priorities and perceptions. Anyone else should feel free to add to it, correct it, etc.