Mapping Scientific Influence

Ben Schmidt at Sapping Attention has a beautiful post up (with many pretty pictures) and exactly the sort of smart analysis one expects from his blog. Among the most interesting---albeit very tentative--- conclusions he comes to:

The 'while/whilst' result is suggestive, in that it indicates we can track cultural phenomena completely independent of science in the data. (India looks more like America, while Australia and South Africa look more like Britain: that's interesting to me.)
The university and city stuff can be interesting as well if we look in the right places. Obviously no one cares that "Harvard" is used more than "Stanford" in Cambridge; but the higher results for Stanford near CERN, and for Harvard--to stretch--in Australia may be telling us interesting things about the way that a project like the SLAC can get international recognition.

In Schmidt's hands, the ArXiv becomes a tool for seeing scientific connections inside and across national boundaries. It's fascinating stuff that begs more attention and more research.

Schmidt's work is a great example---on the more technically proficient side---of what James Grossman called for in his AHA executive director column in March 2012 Perspectives:

I am not suggesting that we all become statisticians. But data from the past—even the immediate past—are neither straightforward substance nor transparent material. Organizing piles of scraps of information into a coherent argument is no easy task. This is why it takes a long time to research and write a good history dissertation. Whether or not we have a facility with numbers, we are good at asking questions and analyzing evidence that by its nature generates many variables at once. And because we look for stories—for ways of synthesizing diverse strands into narrative themes—we usually look for interactions among variables that to other eyes might not seem related. By casting our insights into the form of narratives, we also make them more accessible than multivariate regression analyses could ever be—and arguably more amenable to uncertainty and ambiguity. I have little doubt that people asking big questions of Big Data would benefit from collaboration with the qualitative and interpretive perspectives historians bring to this kind of enterprise. It is our task to prepare our students for such options, and to convince those beyond the community of historians that we have something to contribute.