In the quest to understand what good reviewing is, perhaps it’s worthwhile to think about what good research is. One way to think about good research is in terms of a producer/consumer model.
In the producer/consumer model of research, for any element of research there are producers (authors and coauthors of papers, for example) and consumers (people who use the papers to make new papers or code solving problems). An produced bit of research is judged as “good” if it is used by many consumers. There are two basic questions which immediately arise:
- Is this a good model of research?
- Are there alternatives?
The producer/consumer model has some difficulties which can be (partially) addressed.
- Disconnect. A group of people doing research on some subject may become disconnected from the rest of the world. Each person uses the research of other people in the group so it appears good research is being done, but the group has no impact on the rest of the world. One way to detect this is by looking at the consumers2 (the consumers of the consumers) and higher order powers. If the set doesn’t expand much with higher order powers, then there is a disconnect.
- Latency. It is extraordinarily difficult to determine in advance whether a piece of research will have many consumers. A particular piece of work may be useful only after a very long period of time. This difficulty is particularly severe for theoretical research.
- Self-fulfillment To some extent, interesting research by this definition is simply research presented to the largest possible audience. The odds that someone will build on the research are simply larger when it is presented to a larger audience. Some portion of this effect is “ok”—certainly attempting to educate other people is a good idea. But in judging the value of a piece of research, discounting by the vigor with which it is presented may be healthy for the system. (In effect, this as a bias against spamming.)
If we accept the difficulties of the producer consumer model, then good reviewing becomes a problem of predicting what research will have a large impact in terms of the numbers of consumers (and consumers^2, etc…) Citations can act (to some extent) as a proxy for consumption implying that it may be possible to (retroactively) score a reviewer’s judgement. There are many difficulties here. For example a citation of the form “[joe blow 93] is wrong and here’s why” isn’t an example of the sort of use we want to encourage. Another important effect is that a reviewer who rejects a paper biases the number of citations a paper later recieves. Another is that a rejected paper that has been resubmitted to another place may change so that it is simply a better paper. It isn’t obvious what a good method is for taking all of these effects into account.
Clearly, there are problems with this model for judging research (and at the second order, judgements of reviews of research). However, I am not aware of any other abstract model for “good research” which is even this good. If you know one, please comment.
I don’t know who’s seen this, but I found the following link from Lawrence Saul: Publish or Perish–An Ailing Enterprise?, about the proliferation of publications (mostly centered around journals, since the author is a Physicist).
Though I encourage everyone to read the entire article, one interesting tidbit near the end that he suggests is for resumes to only include the listing of 5-10 publications (that is, resumes read for the sake of NSF grands, tenure, jobs, etc…). This seems like quite a nice solution, since it would have to be adopted by far fewer entities (essentially, NSF and all universities) than other methods (which would have to be adopted by all researchers, who might have alterior motives). The advantages to this solution are many, basically stemming from the fact that if you only list 5-10 pubs, there is no reason to republished the same material over and over, to publish junk, or to do any of the other things that we all hate. This, in turn, would cut down on the number of reviews required, and (hopefully) therefore make them better.