During lunch, I stumbled across an interesting discussion between Richard Dawkins and Lawrence Krauss that wanders in a couple different directions, but starts with an interesting way of thinking about how to compare the genius of a man like Darwin with the genius of a man like Einstein.
Dawkins says that we can think of a scientific theory as a ratio between what can be explained and what must be assumed. Dawkins argues that Darwin wins out over Einstein on this ratio, but also notes that this has implications for the underlying genius of Einstein. He jokes "any fool could be a Darwin". Krauss disagrees somewhat and gives Darwin more credit, and the discussion goes on from there.
But I think this ratio idea is a good way to think about science. It's appealing because in a lot of ways it is a productivity measure - informational output per unit of informational input. It really explodes the hard/soft science distinction as well. Yes, social sciences have complex, imperfect, imprecise informational inputs - but the phenomenon it is trying to explain is equally complex, imperfect, and imprecise. The "hardness" of a science doesn't make sense under this schema (or at least it doesn't seem important... all "hardness" really means is "complexity/precision") because we are normalizing outputs with the input.
This also presents a nice way of thinking about scientific advances. Copernicus revolutionized cosmology because he was able to produce the same output (the observed course of astronomical bodies) with substantially fewer inputs. One way of thinking about this is that Copernicus was "right" where others were "wrong"... but as science has advanced we've realized that that sort of hubris may be uncalled for. A better thing to say than that Copernicus was "right" was that he was "more efficient". His theory had higher scientific productivity.
Without having read any Kuhn, this sounds Kuhnian to me - but I think the expression of this ratio as the scientific productivity of a theory has other advantages. After all, with a productivity measure you can have a marginal productivity measure (or really an expected marginal productivity measure because science is a discovery process not really a production process). Once you have a marginal productivity measure you can make all kinds of claims about the behavior of scientists.
I see a few wrinkles that may present obstacles in making use of this sort of marginal productivity measure, but I have to chew on it for a little while. Thoughts or claims to the effect of "ya - so and so essentially already said that" would be appreciated - I'm not as well read as I'd like to be on philosophy/history of science.