# Crossing the Gender Binary: Modelling Gendered Reference in Science Fiction and General Fiction

arXiv: https://arxiv.org/abs/2610.19028 · HTML: https://mlsanity.com/paper/2610.19028/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 65 references that are in the map.
- First posted on arXiv 13 Oct 2026.

## Builds on (most cited first, 9 of 9)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [Living Machines: A study of atypical animacy](https://mlsanity.com/paper/2005.11140/) (2020)
- [Can Language Models Represent the Past without Anachronism?](https://mlsanity.com/paper/2505.00030/) (2025)
- [Provocations from the Humanities for Generative AI Research](https://mlsanity.com/paper/2502.19190/) (2025)
- [Pretraining Language Models for Diachronic Linguistic Change Discovery](https://mlsanity.com/paper/2504.05523/) (2025)
- [Metadata Might Make Language Models Better](https://mlsanity.com/paper/2211.10086/) (2022)
- [Estranged Predictions: Measuring Semantic Category Disruption with Masked Language Modelling](https://mlsanity.com/paper/2511.08109/) (2025)

## Abstract (arXiv)

Science fiction is widely credited with unsettling gender. This article asks whether that holds at the linguistic level, in how readily gendered reference gives way to its counterpart, a neutral term, or other social category, and whether it separates the genre from general fiction. It does so by masking man/men, woman/women, and person/people across same-period science fiction and general fiction corpora and classifying RoBERTa's five likeliest substitutes by gender. Retention and displacement are permutation-tested over five resampling rounds, and the underlying sentences are read closely. Both corpora cross the gender binary in both directions, but unequally: where woman is masked, the model predicts a masculine term about twice as often as it predicts a feminine term where man is masked. Each mismatch marks a statistical surprise, a word the corpus did not expect there. Against this shared pattern, science fiction shows greater gender permeability than the general fiction corpus, placing man in feminine contexts more often than general fiction while the reverse rate holds steady. Its degendering tendency is stronger too, drawing both gendered terms toward the neutral. Beyond the binary, a masked woman also draws substitutes naming a social category and no gender far more often than a masked man does. In measuring the gender permeability of man, woman, and person in linguistic distribution and testing whether genre changes it, the study gives science fiction studies linguistic evidence for thematic claims and computational literary studies a pipeline applicable to other category vocabularies, such as race and class.