Editing a Woman's Voice

Code

• github.com/causalnlp/ai-scholar-uncertainty-prediction
  OfficialIn paperpytorch★ 6

Abstract

Prior work shows that men and women speak with different levels of confidence, though it's often assumed that these differences are innate or are learned in early childhood. Using academic publishing as a setting, we find that language differences across male and female authors are initially negligible: in first drafts of academic manuscripts, men and women write with similar levels of uncertainty. However, when we trace those early drafts to their published versions, a substantial gender gap in linguistic uncertainty arises. That is, women increase their use of cautionary language through the publication process more than men. We show this increase in the linguistic gender gap varies substantially based on editor assignment. Specifically, our author-to-editor matched dataset allows us to estimate editor-specific fixed effects, capturing how specific editors impact the change in linguistic uncertainty for female authors relative to male authors (the editor's author-gender gap). Editors' author-gender gaps vary widely, and correlate with observable editor characteristics such as societal norms in their country-of-origin, their work history, and the year that they obtained their PhD. Overall, our study suggests that a woman's "voice" is partially shaped by external forces, and it highlights the critical role of editors in shaping how female academics communicate.

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