” ScholarSift is kind of like Turnitin in reverse. It compares the text of a law review article to a huge database of law review articles and tells you which ones are similar. Unsurprisingly, it turns out that machine learning is really good at identifying relevant scholarship. And ScholarSift seems to do a better job at identifying relevant scholarship than pricey legacy platforms like Westlaw and Lexis.
One of the many cool things about ScholarSift is its potential to make legal scholarship more equitable. In legal scholarship, as everywhere, fame begets fame. All too often, fame means the usual suspects get all the attention, and it’s a struggle for marginalized scholars to get the attention they deserve. Unlike other kinds of machine learning programs, which seem almost designed to reinforce unfortunate prejudices, ScholarSift seems to do the opposite, highlighting authors who might otherwise be overlooked. That’s important and valuable. I think Anderson and Wenzel are on to something, and I agree that ScholarSift could improve citation practices in legal scholarship….
Anderson and Wenzel argue that ScholarSift can tell authors which articles to cite. I wonder if it couldn’t also make citations pointless. After all, readers can use ScholarSift, just as well as authors….”