From counting citations to measuring usage: People got in the habit of counting how many papers a research produce. This is silly. Nobody counts how many novels a writer has produced as a way to measure his importance. So people have started counting citations. This is better because it is a bit less obvious how you might game it, but it is still silly because 90% of citations are shallow: most authors haven't even read the paper they are citing. We tend to cite famous authors and famous venues in the hope that some of the prestige will get reflected. So, that's the current state-of-the-art. But why stop there? We have the technology to measure the usage made of a cited paper. If you merely cite a paper "in passing", that's rather easy for a computer to measure this effect. Some citations are more significant: for example it can be an extension of the cited paper. Fairly elementary machine learning techniques should suffice to measure the impact of your papers based on how much following papers build on your results. Why isn't it done?

Update: I wrote a blog post based on this G+ post:
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