The Generative Dissensus of Reading the Feminist Novel, 1995-2020: A Computational Analysis of Interpretive Communities
Bibliographic record
Abstract
This article furthers ongoing work on the merits of the feminist novel’s intrinsic variability by probing its dynamics in four publishing contexts: contemporary anglophone literary criticism, prestigious review publications, marketing materials, and online book reviews by social readers. We explore how these interpretive communities converge and diverge in their assessments of feminist fiction over the past twenty-five years by evaluating articles from the MLA International Bibliography, book reviews in The New York Times, The New Yorker, Times Literary Supp-lement, and other prominent periodicals, blurbs from Amazon, and Goodreads reviews. We trace the feminist novel’s ambivalent fates—or rather, feminist novels’ ambivalent fates—in and across these four domains. To do so, we engage computational methods of topic modeling, most distinctive word analysis, and named entity recognition. We synthesize these quantitative results with qualitative attention to provocative examples from our corpus. In so doing, we consider how literary scholars can develop more robust understandings of what feminism and feminist fiction mean to contemporary readers and what we stand to gain by bringing this diverse interpretive labor into our scholarly conversations.
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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 | 0.015 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".