The Generative Dissensus of Reading the Feminist Novel, 1995-2020: A Computational Analysis of Interpretive Communities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it