SBP: Network Connections in Formal Hiring Processes

这个项目着眼于网络效应如何可能会偏向申请人的明显资格，从而可能会影响招聘结果。因此，它超越了众所周知的与其优点无关的申请人的属性对招聘过程的直接影响。该项目着眼于加州大学STEM教师招聘过程中的特定人口统计属性，但结果将适用于更广泛的招聘，包括学院内外。 发现网络的假设效应将构成一个重要的进步，因为这是一个隐藏的偏见来源，即使可以消除更直接的偏见，也会存在。 该研究的结果将对招聘实践产生明确的影响，这将有助于使STEM的招聘更加精英化，从而扩大STEM的参与。该项目得到了教育和人力资源核心研究计划、社会学计划和扩大参与科学的联合支持。该研究开发了研究密集型大学STEM教师招聘过程中参与者之间个人层面网络联系的指标。 它使用这些数据来测试有关网络连接对招聘过程的影响以及网络如何通过学术劳动力市场的关键人口统计属性来加强不平等的假设。 为了实现这些目标，研究人员将通过增加参与教师招聘过程的个人之间的人与人之间的联系的多项措施来增强教师招聘（EEFR）数据的评估公平性，其中包括教师招聘过程的详细措施。测量了雇佣网络中行动者之间的三种个人层面的网络连接，即合著关系、直接引用学者的著作和间接引用，作者引用类似文献的程度。多变量统计分析通过测试专业网络中人口统计属性的差异的存在，程度和相关性，并通过确定网络连接对STEM教师招聘中个人成果的影响，来检查网络连接在正式招聘过程中的影响。该奖项反映了NSF的法定使命，通过使用基金会的智力价值和更广泛的影响力审查标准进行评估，被认为值得支持。

This project looks at how network effects may bias the apparent qualifications of applicants and thereby may affect hiring outcomes. It goes beyond, therefore, well-known direct effects of attributes of applicants irrelevant to their merit on hiring processes. This project looks at specific demographic attributes in the STEM faculty hiring process at the University of California, but the results will apply to hiring more broadly, both within and outside of the academy. Finding the hypothesized effect of networks will constitute an important advance because this is a hidden source of bias that would be present even if more direct biases could be eliminated. Results of the study will have clear implications for hiring practices that will help to make hiring in STEM more meritocratic and, therefore, broaden participation in STEM. This project was supported jointly by the Education and Human Resources Core Research Program, the Sociology Program, and Science of Broadening Participation.The study develops indicators of individual-level network connections among actors involved in STEM faculty hiring processes at research intensive universities. It uses these data to test hypotheses about the influence of network connections on hiring processes and how networks work to reinforce inequalities by key demographic attributes in the academic labor market. To accomplish these goals the researchers will enhance the Evaluating Equity in Faculty Recruitment (EEFR) data, which includes detailed measures of the faculty hiring process, by adding multiple measures of the person-to-person ties between individuals involved in the faculty hiring processes. Three kinds of individual-level network connections between the actors in the hiring network are measured, those indicated by co-authorship relationships, by direct citation of scholar's work, and by indirect citation, i.e., the extent to which the authors cite similar bodies of literature. Multivariate statistical analyses examine the effects of network connections in formal hiring processes by testing the presence, extent, and correlates of differences by demographic attribute in professional networks and by identifying the influence of network connections on individual outcomes in STEM faculty hiring. The result will be the first systematic analysis of how inequalities according to demographic attribute are influenced by network connections in the STEM faculty hiring process.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.