I see several very dubious classifications. Shouldn’t the great stink be under civil engineering rather than agriculture, and why is Plato’s Atlantis under computer science?
(The worst I've noticed before has been Lawrence of Arabia under History of the Ancient World. Very much 20th century really.)
Several other classifications are arguable -- which I think shows one of the limitations of this technique: it's not possible to iterate + improve.
So instead I've been wondering about using the embeddings of each episode synopsis, and comparing to the embeddings of Dewey subdivisions. I should be able to tune the results better that way.
There's also a technique from Google called CAVs (Concept Activation Vectors) that I'm intrigued about trying -- would love to hear if anybody has experience using this
this says you need an API key but I think I found some way to call this without one… You might be able to improve classification by either incorporating the dewey decimals of the books mentioned on a podcast or fine-tuning a model based on known book titles (or maybe there are book summaries somewhere) to known dewey decimals from OCLC.