Data-driven selection of conference speakers based on scientific impact to achieve gender parity
Data-driven selection of conference speakers based on scientific impact to achieve gender parity
复制标题
根据科学影响力以数据驱动选择会议发言人，以实现性别平等
作者：
A. Vallence;M. Hinder;H. Fujiyama
缺乏多样性限制了科学的发展。因此，科学界和更广泛的社会迫切需要增加多样性，包括性别多样性的方法。我们开发了一种新颖的、数据驱动的方法来选择会议演讲者，根据研究人员、招聘委员会和资助机构经常使用的科学影响指标来确定潜在的演讲者，以令人信服地证明男性和女性在同行评审的科学质量上是平等的。该方法使高质量的会议计划没有性别差异，并为STEM中更广泛的多样性增加产生积极的螺旋。
A lack of diversity limits progression of science. Thus, there is an urgent demand in science and the wider community for approaches that increase diversity, including gender diversity. We developed a novel, data-driven approach to conference speaker selection that identifies potential speakers based on scientific impact metrics that are frequently used by researchers, hiring committees, and funding bodies, to convincingly demonstrate parity in the quality of peer-reviewed science between men and women. The approach enables high quality conference programs without gender disparity, as well as generating a positive spiral for increased diversity more broadly in STEM.
影响因子：
5.5
作者：
Strange, Kevin
通讯作者：
Strange, Kevin