Fish contaminants through the tribal perspective : an ethnography of the Keweenaw Bay Indian Community's tribal fish harvest

It has been well documented that many tribal populations and minority groups across the nation have been identified as being at high risk of the adverse health effects created by consuming fish that have been contaminated with mercury, PCBs, DDT, dioxins, and other chemicals. Although fish consumption advisories are intended to inform fish consumers of risks associated with specific species and water bodies, advisories have been the subject of both environmental injustices and treaty rights’ injustices. This means that understanding fish contaminants, through community perspectives is essential to good environmental policy. This study examined the fish contaminant knowledge, impacts on fishing and fish consumption, and the factors that contribute to harvesting decisions and behaviors in one tribal nation in the Upper Peninsula of Michigan, the Keweenaw Bay Indian Community. Using ethnographic methods, participant observation and semi-structured interviewing, fieldnotes were kept and all interviews were fully transcribed for data analysis. Among seventeen fishermen and women, contaminants are poorly understood, have had a limited impact on subsistence fishing but have had a substantial impact on commercial fishing activity. But ultimately, all decisions and behaviors are based on their own criteria and within a larger context of knowledge and understanding: the historical and cultural context. The historical context revealed that advisories are viewed as another attack on tribal fishing. The cultural context revealed that it is the fundamental guidance and essential framework associated with all harvesting beliefs, values, and traditional lifeways. These results have implications for advisories. ‘Fish’ and ‘contaminants’ appear differently based on the perceptions and priorities of those who encounter them.

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.

THE FANNED FLAMES OF DISCONTENT: A SOLIDARITY-INSPIRED HISTORY OF THE IDENTITY/IDEOLOGY, CULTURAL HISTORY, AND RHETORICAL STRATEGIES OF THE WOBBLIES DURING THE 1916 MINNESOTA IRON ORE STRIKE

Rooted in critical scholarship this dissertation is an interdisciplinary study, which contends that having a history is a basic human right. Advocating a newly conceived and termed, Solidarity-inspired History framework/practice perspective, the dissertation argues for and then delivers a restorative voice to working-class historical actors during the 1916 Minnesota Iron Ore Strike. Utilizing an interdisciplinary methodological framework the dissertation combines research methods from the Humanities and the Social Sciences to form a working-class history that is a corrective to standardized studies of labor in the late 19th and early 20th centuries. Oftentimes class interests and power relationships determine the dominant perspectives or voices established in history and disregard people and organizations that run counter to, or in the face of, customary or traditional American themes of patriotism, the Protestant work ethic, adherence to capitalist dogma, or United States exceptionalism. This dissertation counteracts these traditional narratives with a unique, perhaps even revolutionary, examination of the 1916 Minnesota Iron Ore Strike. The intention of this dissertation's critical perspective is to poke, prod, and prompt academics, historians, and the general public to rethink, and then think again, about the place of those who have been dislocated from or altogether forgotten, misplaced, or underrepresented in the historical record. Thus, the purpose of the dissertation is to give voice to historical actors in the dismembered past. Historical actors who have run counter to traditional American narratives often have their body of "evidence" disjointed or completely dislocated from the story of our nation. This type of disremembering creates an artificial recollection of our collective past, which de-articulates past struggles from contemporary groups seeking solidarity and social justice in the present. Class-conscious actors, immigrants, women, the GLBTQ community, and people of color have the right to be remembered on their own terms using primary sources and resources they produced. Therefore, similar to the Wobblies industrial union and its rank-and-file, this dissertation seeks to fan the flames of discontented historical memory by offering a working-class perspective of the 1916 Strike that seeks to interpret the actions, events, people, and places of the strike anew, thus restoring the voices of these marginalized historical actors.