Fleshing Out Models of Gender in English-Language Novels (1850 – 2000)
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
Distant readers have used predictive modelling to study the strength of the relationship between characterization and binary notions of gender. This essay builds on that research, shedding light on several historical trends concerning anatomical description and its relationship to gender. Some of the evidence suggests that bodily language has long played a larger role in configuring fictional women than it did for fictional men. Other evidence implies that bodily characteristics were increasingly bifurcated along a gender binary, reflecting how characters are more and more physically sorted along a feminine-masculine axis. Taken altogether, this essay unpacks a suggestive correlation: a growing aspect of characterization was increasingly imbricated in heteronormative discourses. By weighing the discrepancies between the evidence presented in this essay, and that of its predecessors, this essay will ultimately suggest that disaggregating statistical models can unfold patterns of literary change that would otherwise remain suppressed.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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