This paper focuses on book reviews at the turn-of-the century United States in order to underline fundamental compatibilities between large-scale, computational methods and book historical approaches. It analyzes a dataset of approximately 2,800 book reviews published in The New York Times between January 1, 1905 and December 31, 1925. Several machine learning scenarios are employed to investigate how the underlying reviews constructed gendered norms for reading and readership. Logistic regression models are trained and tested to evaluate how effectively lemma frequencies predict the perceived or presumed gender of an author under review. The paper discusses four different feature selection scenarios, as follows: (1) No terms removed, (2) Stop words removed, (3) Stop words, gender nouns, and titles removed, and (4) Stop words, gender nouns, titles, and common forenames removed. For each scenario, the top lemma coefficients are discussed and interpreted. Tracing the norms (gendered and gendering) of The New York Times Book Review in the early twentieth century demonstrates that even the summary-driven book reviews played an important role in mediating hierarchies of taste and distinction. Further, the paper seeks to demonstrate that cultural analytics methods can be used to investigate a range of research questions related to authorship, publishing, circulation, and reception.