Metraipolis › archive › dc_24b59fa4aff2

Bridging the Language Gap in Scholarly Data II: Enhancing Gender Inference for Chinese Names — arXiv:2610.23734

Source

arXiv (en)

Original

https://arxiv.org/abs/2610.23734

License (as archived)

CC0-1.0

Archived

2026-10-19T08:43:57.916Z

SHA-256 of the archived text

bd4900c47663d64b8e8e8f1431eb4ca6eb09be8f4769094a46a4daa09820a12b

Stable URL of this page

https://metraipolis.com/documento/dc_24b59fa4aff2

How to cite

Bridging the Language Gap in Scholarly Data II: Enhancing Gender Inference for Chinese Names — arXiv:2610.23734 — arXiv (en), https://arxiv.org/abs/2610.23734 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_24b59fa4aff2 (Metraipolis archive dc_24b59fa4aff2, sha256 bd4900c47663d64b8e8e8f1431eb4ca6eb09be8f4769094a46a4daa09820a12b)

@misc{dc24b59fa4aff2,
  title = {Bridging the Language Gap in Scholarly Data II: Enhancing Gender Inference for Chinese Names — arXiv:2610.23734},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.23734},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_24b59fa4aff2; sha256 bd4900c47663d64b8e8e8f1431eb4ca6eb09be8f4769094a46a4daa09820a12b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.23734. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Perceived gender inference from names is important for inclusive scientometrics research, yet existing methods perform poorly on non-Western scholars. We address this gap for Chinese names, which appear in two forms, Hanzi and Pinyin, its Romanized form (e.g., Zhang Wei, Li Na). In international bibliographic records, names are often available only in Pinyin. However, a single pinyin name can correspond to multiple distinct hanzi characters with different gender associations, so gender inference from Pinyin alone is intrinsically ambiguous. To address this, we first develop two hanzi models, a regression model that produces a continuous gender score from 0 (male) to 1 (female), and a classification model that outputs one of three labels (\em{male}, \em{neutral}, \em{female}). We train them on a sample of hanzi names from Chinese-language physics publications, rated by 11 native Chinese speakers. On this sample, the models achieve an RMSE of $0.09$ and an R$^2$ of $0.85$ (regression), and macro-AUC of $0.97$ and a Cohen's $\kappa$ of $0.81$ (classification), demonstrating high performance that is robust to class imbalance. For pinyin names, we propose a pipeline that aggregates hanzi predictions via transliteration and frequency weighting, together with an ambiguity score that flags names whose gender cannot be resolved from Pinyin alone. Benchmarked against three off-the-shelf tools (Namsor, GPT, and Claude), our models achieve the highest agreement with human annotations for both gender scores and gender labels, in both scripts. Applied to an independent sample of Chinese-named authors in APS journals, our models assign a gender to every name, whereas existing Pinyin-based methods leave between 20\% and 100\% of names unclassified. These findings demonstrate the importance of hanzi-level information for reliable gender inference of Chinese names. Code and data are publicly available.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL) ; Computers and Society (cs.CY); Machine Learning (cs.LG); Social and Information Networks (cs.SI); Physics and Society (physics.soc-ph).
Pagina del lavoro: https://arxiv.org/abs/2610.23734 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T08:39:24.882Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).