SBP: Network Connections in Formal Hiring Processes

该项目着眼于网络效应如何使申请人的表面资格产生偏差，从而可能影响招聘结果。因此，它超越了众所周知的与他们的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，但其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是一个重要的进步，因为这是一个隐藏的偏见来源，即使更直接的偏见可以消除，它也会存在。这项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领域的招聘更加精英化，从而扩大STEM领域的参与度。本项目由教育与人力资源核心研究项目、社会学项目和扩大参与科学共同支持。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连接的指标。它使用这些数据来检验关于网络连接对招聘过程的影响的假设，以及网络如何通过学术劳动力市场的关键人口特征来加强不平等。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际关系的多个指标来增强教师招聘公平评估（EEFR）数据，该数据包括教师招聘过程的详细指标。研究测量了雇佣网络中参与者之间的三种个人层面的网络联系，即共同作者关系、直接引用学者作品和间接引用，即作者引用相似文献的程度。多元统计分析通过测试专业网络中人口统计属性差异的存在、程度和相关性，以及通过确定网络连接对STEM教师招聘中个人结果的影响，来检查网络连接在正式招聘过程中的影响。结果将是第一个系统分析根据人口统计属性的不平等如何受到STEM教师招聘过程中网络连接的影响。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，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.