Faculty of Arts and Science Projects

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https://opus.uleth.ca/handle/10133/5439

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    Agawaateyaa (illuminating shadows): reconciling Indigenous and Canadian property systems
    (Lethbridge, Alta. : University of Lethbridge, Dept. of Philosophy, 2025) McIntyre, Donald Gordon; University of Lethbridge. Faculty of Arts and Science; McManus, Sheila
    This dissertation examines the legal, philosophical, and cultural foundations of Canadian property law and its persistent exclusion of Indigenous systems of ownership, governance, and knowledge. Drawing on my Anishinaabe and Scottish heritage, I critique colonial constructs such as the Doctrine of Discovery and terra nullius, deconstruct Western property theories from Hobbes and Locke, and contrast them with Indigenous gifting economies and relational land ethics. Using Subaltern Studies, Critical Discourse Analysis, and Indigenous methodologies, I propose “Indigenous Trans-systemics” as a paradigm that transcends binary legal frameworks, advocating for polyphonic, coexistent systems. Combining scholarly analysis with artistic praxis, I present a holistic, relational model of property rooted in Indigenous cosmologies, ceremony, and responsibility. This work challenges the autopoietic nature of Western legal systems and calls for rebalancing the “volume” between Indigenous and settler voices to advance reconciliation and restore Indigenous legal authority.
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    Implementation of a classification algorithm for institutional analysis
    (Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2008, 2008) Sun, Hongliang; University of Lethbridge. Faculty of Arts and Science; Osborn, Wendy; Fiske, Jo-Anne
    The report presents an implemention of a classification algorithm for the Institutional Analysis Project. The algorithm used in this project is the decision tree classification algorithm which uses a gain ratio attribute selectionmethod. The algorithm discovers the hidden rules from the student records, which are used to predict whether or not other students are at risk of dropping out. It is shown that special rules exist in different data sets, each with their natural hidden knowledge. In other words, the rules that are obtained depend on the data that is used for classification. In our preliminary experiments, we show that between 55-78 percent of data with unknown class lables can be correctly classified, using the rules obtained from data whose class labels are known. We feel this is acceptable, given the large number of records, attributes, and attribute values that are used in the experiments. The project results are useful for large data set analysis.