Hmm, footnotes are like tiny markers in a larger map. If you treat each note as a coordinate—position in the page, length, and maybe the number of words—you could run a clustering algorithm, like k-means, and see if they fall into distinct groups. The challenge is the noise: sometimes the author puts a note for a joke, other times for a key plot point. A simple way to start is to assign each footnote a score based on its proximity to the main text, then plot that score against the page number. If you see a repeating wave or a cluster, that might be the pattern you’re looking for. Try a quick script in Python with pandas and see what the histogram looks like.