Doctoral Dissertation Research: Social Networks and Social Capital: New Techniques for Studying Marginalized Populations at Risk

据报道，在中国，艾滋病毒感染自1994年以来以每年30%的速度增长，人们对这种迅速蔓延的流行病产生了极大的担忧。在这项研究中，研究生Yeon Yu在James Holland Jones博士的指导下，将对被认为参与艾滋病毒传播的边缘人群的社会网络进行研究。识别这些网络对于提供一种新的透镜来检查疾病传播至关重要。同样重要的是，这项研究将有助于开发研究边缘人口的新方法。除了网络数据，研究人员还将收集边缘人口的历史信息，影响他们从事危险工作决定的因素，以及自20世纪70年代末中国新自由主义引发的大规模变革对这些因素的影响。这些因素包括社会经济环境、政治经济、个人关系网以及国家政策。该项目的方法论贡献来自于采用新技术来确定集群边界的属性和说明隐藏人口的社会空间。中国对回应者驱动抽样的人种学应用最终可能成为将回应者驱动抽样应用于其他隐藏或敏感人群研究的原型。研究数据将被用作经验性的参数，以驱动用于流行病学目的的数学模型。这些研究成果最终将使处于边缘地位的高危人群以及广大人口受益。

In China, HIV infections have reportedly risen by 30 percent a year since 1994 and tremendous concern has grown over the burgeoning epidemic. In this research, graduate student Yeon Yu, supervised by Dr. James Holland Jones, will undertake research on the social networks of a marginalized population thought to be involved in HIV transmission. Identifying these networks is critical to providing a new lens to examine disease transmission. Equally important, the research will contribute to developing new methodologies for studying marginalized populations.In addition to the network data, the researcher will collect information about the marginalized population's histories, the forces influencing their decisions to engage in dangerous work, and how these forces have been influenced by the massive transformations stemming from China's neoliberalism since the late 1970s. Among these forces are socioeconomic environments, the political economy, personal networks, as well as state policies. The project's methodological contribution comes from deploying new techniques for identifying the properties of cluster boundaries and illustrating the social space of hidden populations. This ethnographic application of Respondent-Driven Sampling (RDS) in China may ultimately become the prototype for applying RDS in studying other hidden or sensitive populations. The research data will be used as empirically grounded parameters to drive mathematical models for epidemiological purposes. These research results will ultimately benefit marginalized people at risk as well as populations at large.