On the contrary, I've sat on Ivy League tenure panels where humanities members intoned solemnly about H-indices, oblivious to the differences in value of the Li

On the contrary, I've sat on Ivy League tenure panels where humanities members intoned solemnly about H-indices, oblivious to the differences in value of the Lira and the Pound. I also consider much of baseball statistics to be junk science, but at least they have "wins over replacement" that could help with the endless "does author order matter in this subfield?" debates. I welcome a platform that could allow more sophisticated mind-mapping. I've argued to no avail for years that MathSciNet should be open to machine learning, try to make itself a premier playground for machine learning, but it's an AMS cash cow and leadership prefers a 19th century model. Out to lunch recently with a former AMS president, he suggested I should do this for the ArXiv. That's not the point; one wants to encourage significant existing projects to land on our data. Math is pretty much bifurcated between tightly-bound field tribes, only reading papers within their field, and roving players who recognize ideas in any form and chase them across fields. Of course, tribal mathematicians would dispute this. Better mind-mapping would reveal the connections between what's out there, rather than imposing a tribal mindset on its organization. It would be good for mathematics to change this culture. It takes more courage than one might imagine to be a roving player. At hiring time, most departments let the tribes take turns. Just like growing up on the streets, pick a gang for survival, if you want to get hired.