In so many ways, academic work is hard to recognize as being work in the standard wage-labor sense of that word. It can take place at all hours of day or night, outside of standard workplaces, without wearing standard work clothing — in bed with the laptop at midnight, perhaps. American popular stereotypes allege that teaching is outside the realm of productive action and thus second-rate — “those who can’t do, teach.” That’s a maxim which devalues the feminine work of reproduction in favor of an implicitly masculine image of labor, but I digress; my point here is just that such claims reinforce the image of academic work as being in a world of its own.
Earlier this year, I observed that there are two kinds of scholarly overproduction, “herd” overproduction and “star” overproduction. I’d like to come back to that line of thought to push it a bit farther.
I previously argued that if academic overproduction is in many ways market-like we might want to push for a better regulated market in knowledge. I suggested that this could be a complementary strategy to the usual denunciations of market forms in academic life. There is nothing the matter with critiques of market forms, I will stress again; but for all that, they need not be the end point of our thinking.
Continuing that line of thought, I’m wondering whether mass overproduction of academic knowledge may not have some unexpected effects. Its most obvious effect, of course, is the massive amount of “waste knowledge” it generates — the papers that are never read (or barely), citation for its own sake, prolixity for institutional or career reasons, pressures to publish half-finished or mediocre work, etc. All of these are the seemingly “bad” effects of mass overproduction.
But does mass overproduction have any clearly good effects? I like to imagine that one day, machine learning will advance to the point where all the unread scholarly papers of the early 21st century will become accessible to new syntheses, new forms of searching, and so on. We don’t know how our unread work might be used in the future; perhaps it will be a useful archive for someone.
More immediately, I’m also wondering if mass overproduction is creating new forms of self-consciousness in the present. In Anglophone cultural anthropology, it seems to me that mass overproduction is forcing us to constantly ask “what is at stake here?” Older scholarship seldom needed to ask itself that question, as far as I can tell, and certainly not routinely, with every article published. It became common, somewhere along the way, to ask, “so what?”
When you spend a few years writing code, the principles of programming can start to spill over into other parts of your life. Programming has so many of its own names, its own procedures, its little rituals. Some of them are (as anthropologists like to say) “good to think with,” providing useful metaphors that we can take elsewhere.
I’ve gotten interested in programming as a stock of useful metaphors for thinking about intellectual labor. Here I want to think about scholarly reading in terms of what programmers call caching. Never heard of caching? Here’s what Wikipedia says:
In computing, a cache is a component that stores data so future requests for that data can be served faster; the data stored in a cache might be the result of an earlier computation, or the duplicate of data stored elsewhere. A cache hit occurs when the requested data can be found in a cache, while a cache miss occurs when it cannot. Cache hits are served by reading data from the cache, which is faster than recomputing a result or reading from a slower data store; thus, the more requests can be served from the cache, the faster the system performs.
Basically the idea is that, if you need information about X, and it is time-consuming to get that information, then it makes more sense to look up X once and then keep the results nearby for future use. That way, if you refer to X over and over, you don’t waste time retrieving it again and again. You just look up X in your cache; the cache is designed to be quick to access.
Failed research projects ought to count for something! It’s too bad they don’t. They just disappear into nowhere, it seems to me: into filing cabinets, abandoned notebooks, or forgotten folders on some computer. The data goes nowhere; nothing is published about it and no talks are given; no blog posts are written and no credit is claimed. You stop telling anyone you’re working on your dead projects, once they’re dead.
I’m imagining here that other social researchers are like me: they have a lot of ideas for research projects, but only some of them come to fruition. Here are some of mine:
- Interview project on the personal experience of people applying to graduate school in English and Physics. (It got started, but didn’t have a successful strategy for subject recruitment.)
- Interview project on student representatives to university Boards of Trustees in the Chicago area. (I got started with this, but didn’t have the time to continue.)
- Historical research project on what I hypothesized was a long-term decline of organized campus labor at the University of Chicago. (I only ever did some preliminary archival poking around.)
- Project on faculty homes in the Paris region. (I only had fragmentary data about this, and it was too hard to collect more, and never the main focus of my work.)
- Discourse analysis project on “bad writing” in the U.S. humanities. (I did write my MA thesis about this topic, but it needed a lot more work to continue, and for now it just sits there, half-dead.)
I’ve been thinking about certain scholars who have written, for lack of a more precise way of putting it, a lot. The sort of people who seem to write a book a year for thirty years. I don’t necessarily mean scholars in, say, the laboratory sciences, but more like the humanists, the anthropologists, the philosophers. Today a post by Brian Leiter quoting a caustic review of the prolific scholar Steve Fuller reminded me of the topic.
If one description of scholarly activity is “producing knowledge,” then logically, wouldn’t we expect that there would be such a thing as “overproducing knowledge”? Can there be an overproduction crisis of scholarship?