title: Strategic framing: indigenous culture, identity, and politics

authors: Schoenberger-Orgad, Michèle Anne

subjects: New Zealand

abstract: This article analyses the role played by culture and the strategic communication of culture in the formation and establishment of the Māori Party. As an indigenous grassroots party, which achieved electoral success in the 2005, general election in New Zealand, the Māori Party offers an unusual case of cultural primacy as a political party for indigenous people. The Māori Party campaigned to be the first successful party to give a distinctive voice to Māori in a parliament dominated by Pakeha (New Zealand Europeans).

In examining the Māori Party's formation and campaign as a case study, the article focuses on socio-political conditions, issues of national identity, media changes, and the cultural relations between Māori and Pakeha. It pays particular attention to those that influenced public opinion during the 14 months of election campaigning. It argues that the strategic framing of the Māori Party as a movement giving a distinctive voice for Māori established the foundations for the campaign's success. In conclusion, the significance of the achievements of the Māori Party in the 2008 general election are discussed as evidence of the now distinct and recognized representation of Māori within the New Zealand democratic process.

text: Strategic framing: indigenous culture, identity, and politics This article analyses the role played by culture and the strategic communication of culture in the formation and establishment of the Māori Party. As an indigenous grassroots party, which achieved electoral success in the 2005, general election in New Zealand, the Māori Party offers an unusual case of cultural primacy as a political party for indigenous people. The Māori Party campaigned to be the first successful party to give a distinctive voice to Māori in a parliament dominated by Pakeha (New Zealand Europeans).

In examining the Māori Party's formation and campaign as a case study, the article focuses on socio-political conditions, issues of national identity, media changes, and the cultural relations between Māori and Pakeha. It pays particular attention to those that influenced public opinion during the 14 months of election campaigning. It argues that the strategic framing of the Māori Party as a movement giving a distinctive voice for Māori established the foundations for the campaign's success. In conclusion, the significance of the achievements of the Māori Party in the 2008 general election are discussed as evidence of the now distinct and recognized representation of Māori within the New Zealand democratic process.

year: 2011

title: Civilisation, settlers and wanderers: Law, politics and mobility in nineteenth century New Zealand and Australia

authors: Seuffert, Nan

subjects: colonization

abstract: Mobility was constitutive of the 19th century British colonial period in the Pacific. The circulation of capital and commodities, technologies of transportation and communication, travelling ideologies and systems of governance and surveillance, as well as the movement of explorers, whalers, labourers, settlers, missionaries, colonial administrators, convicts, soldiers, sojourners, immigrants, and transnational and displaced indigenous peoples, all shaped the politics and the period (see Ballantyne 2009: 7-8; Seuffert 2006: 7-8; Arrighi 1994: 48-58). Highly mobile British and European immigrants with money or skills were termed 'settlers', with associated connotations of stabilising and civilising influences. Missionaries and colonial officials who were integral to colonisation were also often highly mobile, carrying policies and regulatory regimes with them, and their colonial roles as 'civilising' influences included 'settling' and advancing the position of indigenous and other colonised peoples. In contrast, the mobility of poor whites and racialised populations - such as 'Melanesian' indentured labourers (Banivanua-Mar 2007: 3-4)2, Indian workers, displaced indigenous peoples and 'sojourner' Chinese - attracted the attention of law (and policy) makers and institutional authorities and were subjected to various forms of surveillance, regulation and policing designed to constrain and contain them.

text: Civilisation, settlers and wanderers: Law, politics and mobility in nineteenth century New Zealand and Australia Mobility was constitutive of the 19th century British colonial period in the Pacific. The circulation of capital and commodities, technologies of transportation and communication, travelling ideologies and systems of governance and surveillance, as well as the movement of explorers, whalers, labourers, settlers, missionaries, colonial administrators, convicts, soldiers, sojourners, immigrants, and transnational and displaced indigenous peoples, all shaped the politics and the period (see Ballantyne 2009: 7-8; Seuffert 2006: 7-8; Arrighi 1994: 48-58). Highly mobile British and European immigrants with money or skills were termed 'settlers', with associated connotations of stabilising and civilising influences. Missionaries and colonial officials who were integral to colonisation were also often highly mobile, carrying policies and regulatory regimes with them, and their colonial roles as 'civilising' influences included 'settling' and advancing the position of indigenous and other colonised peoples. In contrast, the mobility of poor whites and racialised populations - such as 'Melanesian' indentured labourers (Banivanua-Mar 2007: 3-4)2, Indian workers, displaced indigenous peoples and 'sojourner' Chinese - attracted the attention of law (and policy) makers and institutional authorities and were subjected to various forms of surveillance, regulation and policing designed to constrain and contain them.

year: 2011

title: Learning discrete and Lipschitz representations

authors: Gouk, Henry

subjects: Machine Learning

abstract: Learning to embed data into a low dimensional vector space that is more useful for some downstream task is one of the most common problems addressed in the representation learning literature. Conventional approaches to solving this problem typically rely on training neural networks using labelled training data. In order to construct an accurate embedding function that will generalise to data not seen during training, one must either gather a very large training dataset, or adequately bias the learning process. This thesis focuses on the task of incorporating new inductive biases into the representation learning paradigm by constraining the set of functions that a learned feature extractor can come from.

The first part of this thesis investigates how one can learn a mapping that changes slowly with respect to its input. This is first addressed by deriving the Lipschitz constant of common feed-forward neural network architectures, and subsequently demonstrating how this constant can be constrained during training. Following this, it is investigated how a similar goal can be accomplished when one assumes that the inputs of interest lie near a low dimensional manifold embedded in a high dimensional vector space. This results in an algorithm that takes advantage of an empirical analog to the Lipschitz constant. Experimental results show that these methods have favourable performance compared to other methods commonly used for imposing inductive biases on neural network learning algorithms.

In the second part of this thesis, methods for extracting representations using decision tree models are developed. The first method presented is a problem transformation approach that allows one to reuse existing tree induction techniques. The second approach shows how one can incrementally construct decision trees using gradient information as the source of supervision, allowing one to use an ensemble of decision trees as a layer in a neural network. The experimental results indicate that these approaches improve the performance of representation learning on tabular data across multiple tasks.

text: Learning discrete and Lipschitz representations Learning to embed data into a low dimensional vector space that is more useful for some downstream task is one of the most common problems addressed in the representation learning literature. Conventional approaches to solving this problem typically rely on training neural networks using labelled training data. In order to construct an accurate embedding function that will generalise to data not seen during training, one must either gather a very large training dataset, or adequately bias the learning process. This thesis focuses on the task of incorporating new inductive biases into the representation learning paradigm by constraining the set of functions that a learned feature extractor can come from.

The first part of this thesis investigates how one can learn a mapping that changes slowly with respect to its input. This is first addressed by deriving the Lipschitz constant of common feed-forward neural network architectures, and subsequently demonstrating how this constant can be constrained during training. Following this, it is investigated how a similar goal can be accomplished when one assumes that the inputs of interest lie near a low dimensional manifold embedded in a high dimensional vector space. This results in an algorithm that takes advantage of an empirical analog to the Lipschitz constant. Experimental results show that these methods have favourable performance compared to other methods commonly used for imposing inductive biases on neural network learning algorithms.

In the second part of this thesis, methods for extracting representations using decision tree models are developed. The first method presented is a problem transformation approach that allows one to reuse existing tree induction techniques. The second approach shows how one can incrementally construct decision trees using gradient information as the source of supervision, allowing one to use an ensemble of decision trees as a layer in a neural network. The experimental results indicate that these approaches improve the performance of representation learning on tabular data across multiple tasks.

year: 2019