Numbers appear to have limited value for literary study, since our discipline is usually more concerned to explore differences of interpretation than to describe the objective features of literary works. But it may be time to re-examine the assumption that numbers are only useful for objective description. Machine learning algorithms are actually bad at being objective, and rather good at absorbing human perspectives implicit in the evidence used to train them. To dramatize perspectival uses of machine learning, I train models of genre on groups of books categorized by historical actors who range from Edwardian advertisers to contemporary librarians. Comparing the perspectives implicit in their choices casts new light on received histories of genre. Scientific romance and science fiction—whose shifting names have often suggested a fractured history—turn out to be more stable across two centuries than the genre we call fantasy.