title: The atomisation of the American Left: The unravelling of collectivism in protesting United States’ Foreign Policy

authors: Mikkelson, Vaughan

abstract: This thesis addresses the question: has the rise of postmodernism and the New Social Movements essentially undermined and “individuated” the traditionally collectivist American Left?  The thesis seeks to address the shift of degree, if any, of the American Left’s character as a collective force of dissent regarding, primarily, U.S. foreign policy.  The approach of this thesis has been to examine two indicative and comparative case studies, two left-leaning activist groups that emerged spontaneously in the 1980s and 2000s in one of the most conservative areas of the United States, with a view to determining the impact of postmodernism and the New Social Movements on the attitudes and behaviours of the group members.  The approach taken, for the purposes of this thesis, has been to investigate through non-random interviews, based upon an open-ended questionnaire, these activist groups protesting U.S. Foreign Policy.  The research findings posit that the American Left has become atomised in regards to the central question and the rise of postmodernism and the New Social Movements have undermined and ‘individuated’ the traditionally collectivist American Left.  This has been evident for some time as the diffusion of the New Social Movements from the 1970s onwards has seen the Left fragment into single issue groups.  The American Left has become individuated and has migrated away from collectivism to what appears to be a postmodern sensibility, placing ever greater emphasis on self-development and individuality. The American Left has apparently retreated into forms of localism. For example: issues pertaining to municipalities, the academy and  the arts.  Also, the Left has had a visible presence through single-issue platforms such as: the environmental movement, the anti-nuclear movement, gay and lesbian rights and the feminist movement, to name but a few.  Neo-liberals have strongly espoused free market doctrines from a fundamentalist perspective. Neoconservatives have advocated a vision for U.S. foreign policy based on “American values” and the projection of the Liberal “Democratic” project. Neoconservatives, too, possess a fundamentalist ardour. The American Left, by contrast, has found itself perpetually in defensive mode. Generally speaking, American leftists are supporters of the U.S. Democratic Party. The trajectory of the Democratic Party may be seen as having advocated for the cultural Left on the fringes of its social policy platform. However, few voices that espouse traditional Leftist values remain in its ranks.  Therefore, the American left remains a voice in the wilderness buffeted by both the forces of Liberalism and Conservatism.

text: The atomisation of the American Left: The unravelling of collectivism in protesting United States’ Foreign Policy This thesis addresses the question: has the rise of postmodernism and the New Social Movements essentially undermined and “individuated” the traditionally collectivist American Left?  The thesis seeks to address the shift of degree, if any, of the American Left’s character as a collective force of dissent regarding, primarily, U.S. foreign policy.  The approach of this thesis has been to examine two indicative and comparative case studies, two left-leaning activist groups that emerged spontaneously in the 1980s and 2000s in one of the most conservative areas of the United States, with a view to determining the impact of postmodernism and the New Social Movements on the attitudes and behaviours of the group members.  The approach taken, for the purposes of this thesis, has been to investigate through non-random interviews, based upon an open-ended questionnaire, these activist groups protesting U.S. Foreign Policy.  The research findings posit that the American Left has become atomised in regards to the central question and the rise of postmodernism and the New Social Movements have undermined and ‘individuated’ the traditionally collectivist American Left.  This has been evident for some time as the diffusion of the New Social Movements from the 1970s onwards has seen the Left fragment into single issue groups.  The American Left has become individuated and has migrated away from collectivism to what appears to be a postmodern sensibility, placing ever greater emphasis on self-development and individuality. The American Left has apparently retreated into forms of localism. For example: issues pertaining to municipalities, the academy and  the arts.  Also, the Left has had a visible presence through single-issue platforms such as: the environmental movement, the anti-nuclear movement, gay and lesbian rights and the feminist movement, to name but a few.  Neo-liberals have strongly espoused free market doctrines from a fundamentalist perspective. Neoconservatives have advocated a vision for U.S. foreign policy based on “American values” and the projection of the Liberal “Democratic” project. Neoconservatives, too, possess a fundamentalist ardour. The American Left, by contrast, has found itself perpetually in defensive mode. Generally speaking, American leftists are supporters of the U.S. Democratic Party. The trajectory of the Democratic Party may be seen as having advocated for the cultural Left on the fringes of its social policy platform. However, few voices that espouse traditional Leftist values remain in its ranks.  Therefore, the American left remains a voice in the wilderness buffeted by both the forces of Liberalism and Conservatism.

year: 2012

title: Optimal decision trees via search: A reinforcement learning framework

authors: Chaouki, Ayman

subjects: Decision Trees

abstract: Decision Trees are one of the most popular models in Machine Learning because of their interpretability, which is especially noticeable for Decision Trees with few splits (or decision rules), a property that is called sparsity. Interpretable models are highly valued in domains where decisions carry substantial ramifications such as health-care and the criminal justice system. For this reason, seeking optimal sparse Decision Trees constitutes a fundamental problem in Interpretable Machine Learning, and a large research effort has been devoted to solving this problem.

Due to the NP-Hard difficulty of the problem, greedy heuristic methods such as C4.5 and CART have been favoured historically, and while fast and scalable, these methods remain unsatisfactory because they forgo the sparsity portion of the problem. In fact, they often lead to suboptimal and overly complex Decision Trees. Nevertheless, many algorithms have been developed in the literature to address the sparsity problem, with recent breakthroughs leveraging Dynamic Programming (DP) and Branch & Bound (B&B) techniques, and causing a paradigm shift from traditional Mathematical Programming to search algorithms. However, most of the proposed DP and B&B methods follow a Depth-First-Search (DFS) strategy, which, while attractive due to its storage-economy capacity, is inefficient because of its uninformative nature. Moreover, DFS necessitates the definition of a maximum depth hyperparameter a priori, and its inefficiency becomes a limiting factor when considering large maximum depths. These concerns were partially addressed with Best-First-Search (BFS) strategies. BFS is appealing because it prunes the search space more aggressively without losing the optimality guarantee, thus enabling it to find the optimal solution faster than DFS. Moreover, BFS does not necessitate fixing a maximum depth hyperparameter, it can run on infinite depth, which is an important advantage that allows practitioners to focus tuning efforts on other hyperparameters. However, these advantages come at the expense of higher memory consumption than DFS. To alleviate this issue, the search strategies of BFS methods have to be formulated as efficiently as possible, in other words, they need to find the optimal solution as quickly as possible. The reason being to tip the balance of their advantages-drawbacks more towards their advantages, outweighing their high memory consumption issue.

The current BFS methods can be improved substantially by reconsidering the sparsity problem under a framework that benefits from its structural properties. Our main contribution in this thesis addresses this concern within a Reinforcement Learning framework. Our BFS algorithm, Branches, benefits from an AO*-type search strategy, which, coupled with our pruning bound we call Purification Bound and several other heuristic considerations, focuses the search on relevant regions of the search space while maintaining the optimality guarantee. This enables Branches to find optimal sparse Decision Trees faster than the state of the art, and as a result alleviate the memory burden that weighs on BFS approaches. We analyse the computational complexity of Branches theoretically by deriving an upper bound on the number of branch evaluations it performs before terminating, and we show this result to be superior to similar analyses from the literature. These findings are further validated through a series of extensive experiments showing Branches to significantly advance the state of the art in terms of number of iterations, runtime, anytime behaviour and scalability potential. This work lays the foundations for future work that can further improve the scalability of these methods.

In addition to this work, we also study the online setting where a data stream is observed rather than a fixed dataset. Unlike the batch setting, the literature on optimal sparse Decision Trees in is scarce for Online Learning. Most approaches adapt greedy heuristics to handling data streams through statistical estimates, hence inheriting their suboptimality issues. On this front, we develop three Monte Carlo Tree Search (MCTS) algorithms with asymptotic optimality guarantees, and we show that they can successfully retrieve the optimal solution in situations where the state of the art methods are practically guaranteed to fail. Furthermore, we adapt Branches to the online setting inducing Online-Branches, an algorithm that satisfies finite-time optimality guarantees rather than asymptotic ones. Our experiments show Online-Branches to outperform our previously developed MCTS methods. Our work on the online setting has the potential to draw more research attention to the problem of seeking optimal sparse Decision Trees when given a data stream. It also lays a solid foundation for these future works through several discussions about the potential issues that arise due to the hypotheses of Online Learning and our proposed solutions.

text: Optimal decision trees via search: A reinforcement learning framework Decision Trees are one of the most popular models in Machine Learning because of their interpretability, which is especially noticeable for Decision Trees with few splits (or decision rules), a property that is called sparsity. Interpretable models are highly valued in domains where decisions carry substantial ramifications such as health-care and the criminal justice system. For this reason, seeking optimal sparse Decision Trees constitutes a fundamental problem in Interpretable Machine Learning, and a large research effort has been devoted to solving this problem.

Due to the NP-Hard difficulty of the problem, greedy heuristic methods such as C4.5 and CART have been favoured historically, and while fast and scalable, these methods remain unsatisfactory because they forgo the sparsity portion of the problem. In fact, they often lead to suboptimal and overly complex Decision Trees. Nevertheless, many algorithms have been developed in the literature to address the sparsity problem, with recent breakthroughs leveraging Dynamic Programming (DP) and Branch & Bound (B&B) techniques, and causing a paradigm shift from traditional Mathematical Programming to search algorithms. However, most of the proposed DP and B&B methods follow a Depth-First-Search (DFS) strategy, which, while attractive due to its storage-economy capacity, is inefficient because of its uninformative nature. Moreover, DFS necessitates the definition of a maximum depth hyperparameter a priori, and its inefficiency becomes a limiting factor when considering large maximum depths. These concerns were partially addressed with Best-First-Search (BFS) strategies. BFS is appealing because it prunes the search space more aggressively without losing the optimality guarantee, thus enabling it to find the optimal solution faster than DFS. Moreover, BFS does not necessitate fixing a maximum depth hyperparameter, it can run on infinite depth, which is an important advantage that allows practitioners to focus tuning efforts on other hyperparameters. However, these advantages come at the expense of higher memory consumption than DFS. To alleviate this issue, the search strategies of BFS methods have to be formulated as efficiently as possible, in other words, they need to find the optimal solution as quickly as possible. The reason being to tip the balance of their advantages-drawbacks more towards their advantages, outweighing their high memory consumption issue.

The current BFS methods can be improved substantially by reconsidering the sparsity problem under a framework that benefits from its structural properties. Our main contribution in this thesis addresses this concern within a Reinforcement Learning framework. Our BFS algorithm, Branches, benefits from an AO*-type search strategy, which, coupled with our pruning bound we call Purification Bound and several other heuristic considerations, focuses the search on relevant regions of the search space while maintaining the optimality guarantee. This enables Branches to find optimal sparse Decision Trees faster than the state of the art, and as a result alleviate the memory burden that weighs on BFS approaches. We analyse the computational complexity of Branches theoretically by deriving an upper bound on the number of branch evaluations it performs before terminating, and we show this result to be superior to similar analyses from the literature. These findings are further validated through a series of extensive experiments showing Branches to significantly advance the state of the art in terms of number of iterations, runtime, anytime behaviour and scalability potential. This work lays the foundations for future work that can further improve the scalability of these methods.

In addition to this work, we also study the online setting where a data stream is observed rather than a fixed dataset. Unlike the batch setting, the literature on optimal sparse Decision Trees in is scarce for Online Learning. Most approaches adapt greedy heuristics to handling data streams through statistical estimates, hence inheriting their suboptimality issues. On this front, we develop three Monte Carlo Tree Search (MCTS) algorithms with asymptotic optimality guarantees, and we show that they can successfully retrieve the optimal solution in situations where the state of the art methods are practically guaranteed to fail. Furthermore, we adapt Branches to the online setting inducing Online-Branches, an algorithm that satisfies finite-time optimality guarantees rather than asymptotic ones. Our experiments show Online-Branches to outperform our previously developed MCTS methods. Our work on the online setting has the potential to draw more research attention to the problem of seeking optimal sparse Decision Trees when given a data stream. It also lays a solid foundation for these future works through several discussions about the potential issues that arise due to the hypotheses of Online Learning and our proposed solutions.

year: 2024

title: Ko au te whenua, te whenua ko au  – I am the land, the land is me: An autoethnographic investigation of a secondary school teacher’s experience seeking to enrich learning in outdoor  education for Māori students.

authors: Townsend, Jane Emma

subjects: place-responsive

abstract: This thesis is my story as an outdoor educator, as a researcher, and a co-participant reflecting on my own actions and experiences as well as those of my students. In this autoethnography I share my revelations and tensions in my role as an outdoor education teacher seeking to enrich the experiences of Māori students.  Māori culture and history have largely been ignored in the outdoor education classrooms and environments of Aotearoa New Zealand. After teaching the subject for ten years I didn’t perceive that I was perpetuating the same invisibility in my own outdoor education course. Over this time a number of questions that had fermented at the back of my mind came to the fore; ‘why are so few Māori students opting to take outdoor education as a senior secondary school subject?’ and ‘how can I make the subject of outdoor education more desirable and appealing to Māori?’ A place-responsive approach incorporates and values traditional ways of learning through the notion of place and the stories attached to them. The cultural context of learning about and through place has the potential to provide learning opportunities that are relevant and meaningful to all learners but particularly Māori. Place-responsive pedagogies allow outdoor educators to create an environment where language, knowledge, culture and values are normal, valid and legitimate – contexts where Māori students can be themselves. Through this research I have found that the implementation of a place responsive approach has had significant implications for Year twelve outdoor education at Mount Maunganui College. The improvement in Māori student achievement and numbers selecting the subject have  been affirming.

Ko au te whenua, te whenua ko au – I am the land, the land is me

text: Ko au te whenua, te whenua ko au  – I am the land, the land is me: An autoethnographic investigation of a secondary school teacher’s experience seeking to enrich learning in outdoor  education for Māori students. This thesis is my story as an outdoor educator, as a researcher, and a co-participant reflecting on my own actions and experiences as well as those of my students. In this autoethnography I share my revelations and tensions in my role as an outdoor education teacher seeking to enrich the experiences of Māori students.  Māori culture and history have largely been ignored in the outdoor education classrooms and environments of Aotearoa New Zealand. After teaching the subject for ten years I didn’t perceive that I was perpetuating the same invisibility in my own outdoor education course. Over this time a number of questions that had fermented at the back of my mind came to the fore; ‘why are so few Māori students opting to take outdoor education as a senior secondary school subject?’ and ‘how can I make the subject of outdoor education more desirable and appealing to Māori?’ A place-responsive approach incorporates and values traditional ways of learning through the notion of place and the stories attached to them. The cultural context of learning about and through place has the potential to provide learning opportunities that are relevant and meaningful to all learners but particularly Māori. Place-responsive pedagogies allow outdoor educators to create an environment where language, knowledge, culture and values are normal, valid and legitimate – contexts where Māori students can be themselves. Through this research I have found that the implementation of a place responsive approach has had significant implications for Year twelve outdoor education at Mount Maunganui College. The improvement in Māori student achievement and numbers selecting the subject have  been affirming.

Ko au te whenua, te whenua ko au – I am the land, the land is me

year: 2014