Meating the social: sharing atiku-euiash in Sheshatshiu, Labrador

This work examines atiku-euiash (caribou meat) sharing practices in Sheshatshiu, Newfoundland and Labrador, and aims to elucidate an overarching question: how do sharing practices participate in the co-constitution of the Innu ‘social’? The ‘social’ is understood in this work as a descriptor that refers to the emergent properties of the Innu collective. The thesis is that sharing practices participate in the co-constitution of the Innu social and enact its boundaries. Inside these boundaries, atiku-euiash is more than simply a food resource: by realizing Innu values of generosity, respect and autonomy, sharing implicates the associations of human, animal, and animal masters that constitute the Innu world. Sharing is connected with the enskilment of the younger generations by their el-ders, and thus with the reproduction of Innu values through time. The ways of sharing are relevant because changes in such practices affect the constitution of the Innu social. Giv-en Euro-Canadian colonization, the Innu are in a fraught social space in which sharing is interrupted by colonization practices and values. Understanding sharing is necessary to develop policies that do not interrupt the reproduction of the Innu world This work uses several research methods: participant observation, sharing surveys, and interviews. It also uses network analysis as sharing practices leave traces of giving and receiving actions and these traces can be represented as a network of givers, receivers and circulating caribou meat. There are two main ways in which caribou is hunted and shared: household-based hunts and community-based hunts. The household-based hunts are organized by the hunters themselves, who are able and willing to hunt. Community-based hunts are completely organized and funded by the SIFN or the Innu Nation. In or-der to understand the differences in the distribution of the two hunt types, the categories of centrality and clustering are used to show how the flow of atiku-eiuash and its associ-ated realization of values and enskilment correlate with different degrees of centralization inside the sharing clusters.

Labeling large scale social media data using budget-driven One-class SVM classification

The social media classification problems draw more and more attention in the past few years. With the rapid development of Internet and the popularity of computers, there is astronomical amount of information in the social network (social media platforms). The datasets are generally large scale and are often corrupted by noise. The presence of noise in training set has strong impact on the performance of supervised learning (classification) techniques. A budget-driven One-class SVM approach is presented in this thesis that is suitable for large scale social media data classification. Our approach is based on an existing online One-class SVM learning algorithm, referred as STOCS (Self-Tuning One-Class SVM) algorithm. To justify our choice, we first analyze the noise-resilient ability of STOCS using synthetic data. The experiments suggest that STOCS is more robust against label noise than several other existing approaches. Next, to handle big data classification problem for social media data, we introduce several budget driven features, which allow the algorithm to be trained within limited time and under limited memory requirement. Besides, the resulting algorithm can be easily adapted to changes in dynamic data with minimal computational cost. Compared with two state-of-the-art approaches, Lib-Linear and kNN, our approach is shown to be competitive with lower requirements of memory and time.