将结构性污名纳入网络分析
Incorporating Structural Stigma into Network Analysis
摘要：
丰富的文献探讨了同质性和其他形式的非均匀混合与个体水平协变量的指数家族随机图（ERGM）框架内的建模。然而，这种有差别的混合并不能充分解释诸如成见等现象，因为成见涉及通过排斥与群体外有联系的人来积极维持社会界限。在这里，我们引入了一个新的统计数据，可以捕捉到这样的效果，使其有可能探测边界维护的潜在存在，超越提名率的简单差异。我们证明了这一统计数据的背景下，在学校课堂上的性别隔离。
英文摘要：
A rich literature has explored the modeling of homophily and other forms of nonuniform mixing associated with individual-level covariates within the exponential family random graph (ERGM) framework. Such differential mixing does not fully explain phenomena such as stigma, however, which involve the active maintenance of social boundaries by ostracism of persons with out-group ties. Here, we introduce a new statistic that allows for such effects to be captured, making it possible to probe for the potential presence of boundary maintenance above and beyond simple differences in nomination rates. We demonstrate this statistic in the context of gender segregation in a school classroom.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
