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arXiv cs.CL· Yuxuan Liu, Haim Dubossarsky, Ruth Ahnert·· 3 天前AI 评分12

科幻小说与普通小说中的性别指代:RoBERTa 掩码实验研究

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

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研究通过掩码 man/woman/person 并用 RoBERTa 预测替代词,比较同期科幻与普通小说语料中的性别指代。两个语料库均双向跨越性别二元,但不对称:woman 被掩码时模型预测男性词的概率约为 man 被掩码时预测女性词的两倍。科幻小说的性别渗透性更强,man 出现在女性语境中的频率高于普通小说,去性别化倾向也更强。

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Abstract: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.
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2610.19028 [cs.CL]
  (or arXiv:2610.19028v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2610.19028

arXiv-issued DOI via DataCite

Submission history

From: Yuxuan Liu [view email]
[v1] Tue, 13 Oct 2026 18:20:57 UTC (1,257 KB)

来源:arXiv cs.CL · arxiv.org