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This paper analyzes the recognition of women's innovative ideas compared to men's using
bibliometric data in economics, mathematics, and sociology. Employing machine learning, I
establish similarities between papers to construct relevant counterfactual citations. On average,
all-female papers receive 10% fewer citations than all-male papers, a disparity reduced by 40%
when considering team sizes and disappearing in most fields with authors' publication records.
Additionally, strong in-group preferences emerge: all-male teams omit more papers with women,
and vice versa. Accounting for publication histories, female scholars are cited 0% (economics)
to 11% (mathematics) less, with early-career women enduring a 9–14% citation penalty.
