Multimembership and Identity in Writing Centers and in Industry: Examining the Work of Reconciliation in the Michigan Tech Multiliteracies Center and at Kimberly-Clark Corporation

Writing center scholarship and practice have approached how issues of identity influence communication but have not fully considered ways of making identity a key feature of writing center research or practice. This dissertation suggests a new way to view identity -- through an experience of "multimembership" or the consideration that each identity is constructed based on the numerous community memberships that make up that identity. Etienne Wenger (1998) proposes that a fully formed identity is ultimately impossible, but it is through the work of reconciling memberships that important individual and community transformations can occur. Since Wenger also argues that reconciliation "is the most significant challenge" for those moving into new communities of practice (or, "engage in a process of collective learning in a shared domain of human endeavor" (4)), yet this challenge often remains tacit, this dissertation examines and makes explicit how this important work is done at two different research sites - a university writing center (the Michigan Tech Multiliteracies Center) and at a multinational corporation (Kimberly-Clark Corporation). Drawing extensively on qualitative ethnographic methods including interview transcriptions, observations, and case studies, as well as work from scholars in writing center studies (Grimm, Denney, Severino), literacy studies (New London Group, Street, Gee), composition (Horner and Trimbur, Canagarajah, Lu), rhetoric (Crowley), and identity studies (Anzaldua, Pratt), I argue that, based on evidence from the two sites, writing centers need to educate tutors to not only take identity into consideration, but to also make individuals' reconciliation work more visible, as it will continue once students and tutors leave the university. Further, as my research at the Michigan Tech Multiliteracies Center and Kimberly-Clark will show, communities can (and should) change their practices in ways that account for reconciliation work as identity, communication, and learning are inextricably bound up with one another.

MODULARITY BASED FUZZY COMMUNITY DETECTION IN SOCIAL NETWORKS

Fuzzy community detection is to identify fuzzy communities in a network, which are groups of vertices in the network such that the membership of a vertex in one community is in [0,1] and that the sum of memberships of vertices in all communities equals to 1. Fuzzy communities are pervasive in social networks, but only a few works have been done for fuzzy community detection. Recently, a one-step forward extension of Newman’s Modularity, the most popular quality function for disjoint community detection, results into the Generalized Modularity (GM) that demonstrates good performance in finding well-known fuzzy communities. Thus, GMis chosen as the quality function in our research. We first propose a generalized fuzzy t-norm modularity to investigate the effect of different fuzzy intersection operators on fuzzy community detection, since the introduction of a fuzzy intersection operation is made feasible by GM. The experimental results show that the Yager operator with a proper parameter value performs better than the product operator in revealing community structure. Then, we focus on how to find optimal fuzzy communities in a network by directly maximizing GM, which we call it Fuzzy Modularity Maximization (FMM) problem. The effort on FMM problem results into the major contribution of this thesis, an efficient and effective GM-based fuzzy community detection method that could automatically discover a fuzzy partition of a network when it is appropriate, which is much better than fuzzy partitions found by existing fuzzy community detection methods, and a crisp partition of a network when appropriate, which is competitive with partitions resulted from the best disjoint community detections up to now. We address FMM problem by iteratively solving a sub-problem called One-Step Modularity Maximization (OSMM). We present two approaches for solving this iterative procedure: a tree-based global optimizer called Find Best Leaf Node (FBLN) and a heuristic-based local optimizer. The OSMM problem is based on a simplified quadratic knapsack problem that can be solved in linear time; thus, a solution of OSMM can be found in linear time. Since the OSMM algorithm is called within FBLN recursively and the structure of the search tree is non-deterministic, we can see that the FMM/FBLN algorithm runs in a time complexity of at least O (n2). So, we also propose several highly efficient and very effective heuristic algorithms namely FMM/H algorithms. We compared our proposed FMM/H algorithms with two state-of-the-art community detection methods, modified MULTICUT Spectral Fuzzy c-Means (MSFCM) and Genetic Algorithm with a Local Search strategy (GALS), on 10 real-world data sets. The experimental results suggest that the H2 variant of FMM/H is the best performing version. The H2 algorithm is very competitive with GALS in producing maximum modularity partitions and performs much better than MSFCM. On all the 10 data sets, H2 is also 2-3 orders of magnitude faster than GALS. Furthermore, by adopting a simply modified version of the H2 algorithm as a mutation operator, we designed a genetic algorithm for fuzzy community detection, namely GAFCD, where elite selection and early termination are applied. The crossover operator is designed to make GAFCD converge fast and to enhance GAFCD’s ability of jumping out of local minimums. Experimental results on all the data sets show that GAFCD uncovers better community structure than GALS.