Gender Dynamics and Critical Reception: A Study of Early 20th-century Book Reviews from The New York Times
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".