Research press release


Nature Astronomy

Gender bias pervades science, even in space



Neven Caplarらは、1950年から2015年に発行されたAstronomy & Astrophysics、The Astrophysical Journal、Monthly Notices of the Royal Astronomical Society、Nature および Science の5種類の雑誌に掲載された149,000編の論文を、機械学習の技術を使用して解析した。本論文の著者たちは、筆頭著者の研究経験年数や所属、論文の著者数、参考文献数、出版年、雑誌名および研究分野などの性別と関係のない特性を組み合わせた後で、筆頭著者が女性の論文と男性の論文との被引用回数を比較した。この比較により、女性を筆頭著者とする論文の引用回数が系統的に低いことが示された。本論文の著者たちは、ジェンダーバイアスをさらに定量化するために、自己引用の傾向や研究者たちのネットワークなど、論文の他の特性と著者とに関する追加解析が必要であると結論づけた。

Astronomy papers with a woman lead author receive 10% fewer citations than similar papers with a lead author that is a man, reports a study published online this week in Nature Astronomy. The finding provides a clear indication that gender bias exists in the field of astronomy.

Previous studies in other scientific fields have shown that papers with a female lead author receive lower review scores compared to identical work led by men, that women are underrepresented in prestigious publications and authorship positions, and that women receive around 10% fewer citations. However, quantifying this effect on citations has been challenging due to the often sparse bibliographic data and multiple compounding factors affecting the number of citations a paper receives.

Neven Caplar and colleagues employ machine-learning techniques to analyse over 149,000 papers published between 1950 and 2015 in five journals: Astronomy & Astrophysics, The Astrophysical Journal, Monthly Notices of the Royal Astronomical Society, Nature and Science. They compare the number of citations received by women-led papers to those led by men after matching the non-gender specific properties of the papers, such as the first author’s seniority and institution, total number of authors, number of references, publication year, journal and field of study. The authors find that this comparison reveals a systematic under-citation of women-led papers. They conclude that additional analysis of other properties of the papers and the authors, such as their self-citation tendency and collaboration network, is needed to further quantify gender bias.

doi: 10.1038/s41550-017-0141

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