title: Dis-lodging literature from English: Challenging linguistic hegemonies

authors: Locke, Terry

subjects: literature

abstract: This paper problematises the location of literature "teaching" within the English (L1) curriculum, as is the case in New Zealand and other settings. It defamiliarises this arrangement by drawing attention to official New Zealand policies of biculturalism and to the increasing cultural and linguistic diversity in many New Zealand classrooms. It identifies a number of social justice issues arising from the current arrangement, and also raises issues in respect of educational policy and ways in which canonical subjects become constructed in practice. It then discusses ways in which a new qualifications template developed at the University of Waikato might provide a vehicle for establishing a new arrangement, in terms of which literature study is dislodged from English and reshaped as a course of study entitled Literature in Society. It indicates ways in which Comparative Literature, as a predominantly university-constituted discipline, might contribute to the theorisation of this new arrangement.

text: Dis-lodging literature from English: Challenging linguistic hegemonies This paper problematises the location of literature "teaching" within the English (L1) curriculum, as is the case in New Zealand and other settings. It defamiliarises this arrangement by drawing attention to official New Zealand policies of biculturalism and to the increasing cultural and linguistic diversity in many New Zealand classrooms. It identifies a number of social justice issues arising from the current arrangement, and also raises issues in respect of educational policy and ways in which canonical subjects become constructed in practice. It then discusses ways in which a new qualifications template developed at the University of Waikato might provide a vehicle for establishing a new arrangement, in terms of which literature study is dislodged from English and reshaped as a course of study entitled Literature in Society. It indicates ways in which Comparative Literature, as a predominantly university-constituted discipline, might contribute to the theorisation of this new arrangement.

year: 2004-05-01

title: Making sense of Te Whāriki a te Kōhanga Reo through toi Māori: A whānau approach

authors: McMillan, Hoana

abstract: The involvement of whānau in the teaching and learning process is important at every juncture of a child’s educational journey. Rich understanding of the curriculum enables whānau to make deep and meaningful contributions to discussions about their child’s development. It also presents an interesting challenge for educational settings to create opportunities for whānau to increase their knowledge of the curriculum. This article outlines the benefits of a toi Māori approach to support whānau understanding.

text: Making sense of Te Whāriki a te Kōhanga Reo through toi Māori: A whānau approach The involvement of whānau in the teaching and learning process is important at every juncture of a child’s educational journey. Rich understanding of the curriculum enables whānau to make deep and meaningful contributions to discussions about their child’s development. It also presents an interesting challenge for educational settings to create opportunities for whānau to increase their knowledge of the curriculum. This article outlines the benefits of a toi Māori approach to support whānau understanding.

year: 2023

title: Linear dimensionality reduction in linear time: Johnson-Lindenstrauss-type guarantees for random subspace

authors: Lim, Nick Jin Sean

subjects: Machine learning

abstract: We consider the problem of efficient randomized dimensionality reduction with norm-preservation guarantees. Specifically we prove data-dependent Johnson-Lindenstrauss-type geometry preservation guarantees for Ho's random subspace method: When data satisfy a mild regularity condition -- the extent of which can be estimated by sampling from the data -- then random subspace approximately preserves the Euclidean geometry of the data with high probability. Our guarantees are of the same order as those for random projection, namely the required dimension for projection is logarithmic in the number of data points, but have a larger constant term in the bound which depends upon this regularity. A challenging situation is when the original data have a sparse representation, since this implies a very large projection dimension is required: We show how this situation can be improved for sparse binary data by applying an efficient `densifying' preprocessing, which neither changes the Euclidean geometry of the data nor requires an explicit matrix-matrix multiplication. We corroborate our theoretical findings with experiments on both dense and sparse high-dimensional datasets from several application domains.

text: Linear dimensionality reduction in linear time: Johnson-Lindenstrauss-type guarantees for random subspace We consider the problem of efficient randomized dimensionality reduction with norm-preservation guarantees. Specifically we prove data-dependent Johnson-Lindenstrauss-type geometry preservation guarantees for Ho's random subspace method: When data satisfy a mild regularity condition -- the extent of which can be estimated by sampling from the data -- then random subspace approximately preserves the Euclidean geometry of the data with high probability. Our guarantees are of the same order as those for random projection, namely the required dimension for projection is logarithmic in the number of data points, but have a larger constant term in the bound which depends upon this regularity. A challenging situation is when the original data have a sparse representation, since this implies a very large projection dimension is required: We show how this situation can be improved for sparse binary data by applying an efficient `densifying' preprocessing, which neither changes the Euclidean geometry of the data nor requires an explicit matrix-matrix multiplication. We corroborate our theoretical findings with experiments on both dense and sparse high-dimensional datasets from several application domains.

year: 2017