Studies that have tested gender trends in this process have found no systematic asymmetries: there appear to be no further deletions of content about women or more nominations to remove content about women (Adams et al., 2019; Worku et al., 2020). That suggests that the main challenge for female representation is in the initial selection process and not in this subsequent review stage.

Panel A shows that editor*female fixed effects explain a substantive portion ofthe variation in changes in uncertainty, and that collectively, the fixed effects are statisticallydifferent from zero.

he Spacy Python Package, using the named entity recognitionsoftware to select a potential editor name.

name-to-gender inference services.

Our sample starts with articles published in the top fifteen economics, finance, accounting,and management journals for the period 2014-2019

how a particular editor influences female-authored papers rel-ative to male-authored papers (the editor’s author-gender gap)

multi-method approach to isolate the role of gender in changes in uncertainty, includingextensive control variables and fixed effects, and by training an NLP model to simu-late all-else-equal counterfactual observations

11 point gap in uncertainty arises.

exam-ining the evolution of cautionary language used in manuscripts over the course of thereview process

Do societal pressures encourage women to be more uncertain than their male coun-terparts?

their self esteem slightly, while men are relativelyunaffected. When receiving criticism, women’s self esteemis substantially decreased as a result, while that of men isagain relatively unchanged [22,34

The latter could be a sign that female notability is sometimes subordinated to male notability processes.

'content gender gap

'Wikidata Human Gender Indicators' (Klein et al., 2016) have supported this work, providing weekly updated information on gender disparities in Wikipedia content and detailing specific statistics about countries, cultures, and historical periods.

In short, we currently have a more complex approach to the content gender gap, with a more abstract definition and linked to new concerns. But that approach still lacks a clear structure of analysis. That is because there is no conceptual framework to integrate all the empirical results on this subject in a theoretically coherent way. For example, there is no explanation of what are the processes and stages of knowledge organisation involved in the formation of this gap, what agents are implicated in the creation of each gender imbalance, in what sense these content asymmetries are components of the same phenomenon (i.e., 'the content gender gap'), or in what way they are 'framing' communication about gender. In other words, we need a theoretical framework that justifies the joint analysis of the researched content asymmetries, links these disparities to agents and their editing processes in Wikipedia, provides meaning to the combined attention to aspects such as the representation, characterisation and structural placement of each gender.

Women in Red, Women in Green, Wiki Loves Women, WikiGap, WikiWomen's Collaborative, WikiHerStory, Editatona, and several others.