Outlets matter: an ethnographic study of an organized clandestine Chinese immigrant social network in the United States
Zhang, Shudong, 1974-
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Recent government statistics shows an astonishing figure of illegal Chinese workers coming into the US every year. Reportedly, it represents only a portion of the Chinese illegally residing in the United States. There exist multiple layers of barriers, such as the Pacific Ocean and other cultural and language obstacles. The question would be why, how, and who kept coming? The ethnographic study of Chinese workers is to reveal the patterns of social network that has been operating well in the process. The network is a broader sense network from societal level, including three dimensions: nodes, ties and outlets. The network must also be treated as the mediator for economic incentive in order to fully understand illegal Chinese immigration in the US. The study also finds that cultural and network patterns are the most important determinant of the illegal Chinese Immigrant to US. A social network analysis exhibits that the network structure is so special and encompasses at least four key outlets in the system. These findings have important theoretical and practical implications. The research contributes to the theoretical development in light of discovering a unique three dimensional network pattern within Chinese ethnic immigrants' culture, different from the universally accepted traditional two elements network analysis-either in a "whole ecological network", or an "egocentric network". In practice, the study findings strongly suggest the significance of such network as to immigration culture. It provides the bedrock for the whole system and gives rise to the issue of illegal immigration. Namely, if the criminal justice system cuts the sustainable network off at any level, there would be no more illegal Chinese immigrants. The findings show specifics of the type of social network that is the fount and matrix of Chinese clandestine immigration systems.
2009 Freely available dissertations (MU)