Manufacturing 'authenticity' : a case study of the Niagara wine cluster

In this thesis, I use "Fabricating Authenticity," a model developed in the Production of Culture Perspective, to explore the evolving criteria for judging what constitute "real" and authentic Niagara wines, along with the naturalization of these criteria, as the Canadian Niagara wine cluster has come under increasing stress from globalization. Authenticity has been identified as a hallmark of contemporary marketing and important to cultural industries, which can use it for creating meaningful differentiation; making it a renewable resource for securing consumers, increasing market value; and for relationships with key brokers. This is important as free trade and international treaties are making traditional protective barriers, like trade tariffs and markups, obsolete and as governments increasingly allocate industry support via promotion and marketing policies that are directly linked to objectives of city and regional development, which in turn carry real implications for what gets to be judged authentic and inauthentic local culture. This research uses a mixed methods research strategy, drawing upon ethnographic observation, marketing materials, newspaper reports, and secondary data to provide insight into the processes and conflicts over efforts to fabricate authenticity, comparing the periods before and after the passage of NAFT A to the present period. The Niagara wine cluster is a good case in point because it has little natural advantage nor was there a tradition of quality table wine making to facilitate the naturalization of authenticity. Geographic industrial clusters have been found particularly competitive in the global economy and the exploratory case study contributes to our understanding of the dynamic of '1abricating authenticity," building on various theoretical propositions to attempt to derive explanations of how global processes affect strategies to create "authenticity," how these strategies affect cultural homogeneity and heterogeneity at the local level, and how the concept of "cluster" contributes to the process of managing authenticity.

Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.

Dynamics in large scale networks

In this thesis we study the properties of two large dynamic networks, the competition network of advertisers on the Google and Bing search engines and the dynamic network of friend relationships among avatars in the massively multiplayer online game (MMOG) Planetside 2. We are particularly interested in removal patterns in these networks. Our main finding is that in both of these networks the nodes which are most commonly removed are minor near isolated nodes. We also investigate the process of merging of two large networks using data captured during the merger of servers of Planetside 2. We found that the original network structures do not really merge but rather they get gradually replaced by newcomers not associated with the original structures. In the final part of the thesis we investigate the concept of motifs in the Barabási-Albert random graph. We establish some bounds on the number of motifs in this graph.