title: Speaking the unspoken: Racism, sport and Māori

authors: Hippolite, Holly Raima

subjects: New Zealand

abstract: In this paper, we consider the silence that surrounds issues of racism in New Zealand sport. We argue that the intersection of two key ideologies – New Zealand’s purported history of good race relations, and the positive contribution that sport is believed to make to racial equality – contribute to a culture of silence in which it is difficult to talk about, let alone discuss constructively, Māori experiences of racism. Our aim is to put the issue on the agenda through engagement with ten experienced Māori sport participants, coaches and administrators whose experiences demonstrate the existence of, and pain caused by, cultural and institutional racism in New Zealand sport. In this aim, we do not seek to hide behind a veil of neutrality or objectivity. Rather, following a kaupapa Māori research approach, our interest is in bringing to light the voices, frustrations and concerns of Māori sportspeople in order to contribute to a much-needed conversation.

text: Speaking the unspoken: Racism, sport and Māori In this paper, we consider the silence that surrounds issues of racism in New Zealand sport. We argue that the intersection of two key ideologies – New Zealand’s purported history of good race relations, and the positive contribution that sport is believed to make to racial equality – contribute to a culture of silence in which it is difficult to talk about, let alone discuss constructively, Māori experiences of racism. Our aim is to put the issue on the agenda through engagement with ten experienced Māori sport participants, coaches and administrators whose experiences demonstrate the existence of, and pain caused by, cultural and institutional racism in New Zealand sport. In this aim, we do not seek to hide behind a veil of neutrality or objectivity. Rather, following a kaupapa Māori research approach, our interest is in bringing to light the voices, frustrations and concerns of Māori sportspeople in order to contribute to a much-needed conversation.

year: 2010

title: Maori and psychology: Research and practice (full conference proceedings)

authors: Robertson, Neville

subjects: Maori

abstract: This is the full proceedings of the symposium sponsored by the Maori and Psychology Research Unit at the University of Waikato: Maori and psychology: Research and practice. The publication of the proceedings of this symposium marks a significant milestone for the Maori and Psychology Research Unit (MPRU). Since its inception in 1997, the unit has played an important role in nurturing and supporting students to engage in Maori-focussed research. They have played, and continue to play, an important part in our research activities as interviewers, analysts, coordinators and writers. 
The MPRU symposium was organised to serve as a platform to profile the work of our students, to disseminate research results and to invite other researchers to witness the nature of the MPRU’s work.

text: Maori and psychology: Research and practice (full conference proceedings) This is the full proceedings of the symposium sponsored by the Maori and Psychology Research Unit at the University of Waikato: Maori and psychology: Research and practice. The publication of the proceedings of this symposium marks a significant milestone for the Maori and Psychology Research Unit (MPRU). Since its inception in 1997, the unit has played an important role in nurturing and supporting students to engage in Maori-focussed research. They have played, and continue to play, an important part in our research activities as interviewers, analysts, coordinators and writers. 
The MPRU symposium was organised to serve as a platform to profile the work of our students, to disseminate research results and to invite other researchers to witness the nature of the MPRU’s work.

year: 1999

title: Improving Hoeffding Trees

authors: Kirkby, Richard Brendon

subjects: machine learning

abstract: Modern information technology allows information to be collected at a far greater rate than ever before. So fast, in fact, that the main problem is making sense of it all. Machine learning offers promise of a solution, but the field mainly focusses on achieving high accuracy when data supply is limited. While this has created sophisticated classification algorithms, many do not cope with increasing data set sizes. When the data set sizes get to a point where they could be considered to represent a continuous supply, or data stream, then incremental classification algorithms are required. In this setting, the effectiveness of an algorithm cannot simply be assessed by accuracy alone. Consideration needs to be given to the memory available to the algorithm and the speed at which data is processed in terms of both the time taken to predict the class of a new data sample and the time taken to include this sample in an incrementally updated classification model.

The Hoeffding tree algorithm is a state-of-the-art method for inducing decision trees from data streams. The aim of this thesis is to improve this algorithm. To measure improvement, a comprehensive framework for evaluating the performance of data stream algorithms is developed. Within the framework memory size is fixed in order to simulate realistic application scenarios. In order to simulate continuous operation, classes of synthetic data are generated providing an evaluation on a large scale. Improvements to many aspects of the Hoeffding tree algorithm are demonstrated. First, a number of methods for handling continuous numeric features are compared. Second, tree prediction strategy is investigated to evaluate the utility of various methods. Finally, the possibility of improving accuracy using ensemble methods is explored.

The experimental results provide meaningful comparisons of accuracy and processing speeds between different modifications of the Hoeffding tree algorithm under various memory limits. The study on numeric attributes demonstrates that sacrificing accuracy for space at the local level often results in improved global accuracy. The prediction strategy shown to perform best adaptively chooses between standard majority class and Naive Bayes prediction in the leaves. The ensemble method investigation shows that combining trees can be worthwhile, but only when sufficient memory is available, and improvement is less likely than in traditional machine learning. In particular, issues are encountered when applying the popular boosting method to streams.

text: Improving Hoeffding Trees Modern information technology allows information to be collected at a far greater rate than ever before. So fast, in fact, that the main problem is making sense of it all. Machine learning offers promise of a solution, but the field mainly focusses on achieving high accuracy when data supply is limited. While this has created sophisticated classification algorithms, many do not cope with increasing data set sizes. When the data set sizes get to a point where they could be considered to represent a continuous supply, or data stream, then incremental classification algorithms are required. In this setting, the effectiveness of an algorithm cannot simply be assessed by accuracy alone. Consideration needs to be given to the memory available to the algorithm and the speed at which data is processed in terms of both the time taken to predict the class of a new data sample and the time taken to include this sample in an incrementally updated classification model.

The Hoeffding tree algorithm is a state-of-the-art method for inducing decision trees from data streams. The aim of this thesis is to improve this algorithm. To measure improvement, a comprehensive framework for evaluating the performance of data stream algorithms is developed. Within the framework memory size is fixed in order to simulate realistic application scenarios. In order to simulate continuous operation, classes of synthetic data are generated providing an evaluation on a large scale. Improvements to many aspects of the Hoeffding tree algorithm are demonstrated. First, a number of methods for handling continuous numeric features are compared. Second, tree prediction strategy is investigated to evaluate the utility of various methods. Finally, the possibility of improving accuracy using ensemble methods is explored.

The experimental results provide meaningful comparisons of accuracy and processing speeds between different modifications of the Hoeffding tree algorithm under various memory limits. The study on numeric attributes demonstrates that sacrificing accuracy for space at the local level often results in improved global accuracy. The prediction strategy shown to perform best adaptively chooses between standard majority class and Naive Bayes prediction in the leaves. The ensemble method investigation shows that combining trees can be worthwhile, but only when sufficient memory is available, and improvement is less likely than in traditional machine learning. In particular, issues are encountered when applying the popular boosting method to streams.

year: 2007