title: Bicultural perspectives on Māori legal research

authors: Mackinnon, Jacquelin

subjects: Māori

abstract: Increasingly, legal research projects undertaken at law school or in practice will involve Māori custom law and/or tikanga Māori.' The role of both Māori custom law and tikanga Māori is most evident in the work of the Māori Land Court in the interpretation and application of legislation relating to Māori land. Increasingly, general statutes incorporate Māori principles and values, such as those to be found in the Resource Management Act 1991, or make explicit reference to the principles of the Treaty of Waitangi. The statutory interpretation function of the Courts in relation to these, and less obvious examples, requires knowledge of tikanga Māori and/or Māori custom law. For any analysis of the work of the Courts or of the legislature, knowledge of tikanga Māori and/or custom law is required. Both Māori custom law and tikanga Māori are preserved by and accessed through the oral tradition. In addition to its role in the Courts and in relation to legislation, the most significant role played by the oral tradition is in the work of the Waitangi Tribunal. The oral tradition also plays an increasingly important part in other areas requiring research. What follows is an introduction to the oral tradition and its role in the legal system of Aotearoa/New Zealand. An understanding of the oral tradition is essential to the construction of a research path that is both ethical and effective.

text: Bicultural perspectives on Māori legal research Increasingly, legal research projects undertaken at law school or in practice will involve Māori custom law and/or tikanga Māori.' The role of both Māori custom law and tikanga Māori is most evident in the work of the Māori Land Court in the interpretation and application of legislation relating to Māori land. Increasingly, general statutes incorporate Māori principles and values, such as those to be found in the Resource Management Act 1991, or make explicit reference to the principles of the Treaty of Waitangi. The statutory interpretation function of the Courts in relation to these, and less obvious examples, requires knowledge of tikanga Māori and/or Māori custom law. For any analysis of the work of the Courts or of the legislature, knowledge of tikanga Māori and/or custom law is required. Both Māori custom law and tikanga Māori are preserved by and accessed through the oral tradition. In addition to its role in the Courts and in relation to legislation, the most significant role played by the oral tradition is in the work of the Waitangi Tribunal. The oral tradition also plays an increasingly important part in other areas requiring research. What follows is an introduction to the oral tradition and its role in the legal system of Aotearoa/New Zealand. An understanding of the oral tradition is essential to the construction of a research path that is both ethical and effective.

year: 2007

title: Matariki: The star of the year by Rangi Matamua [Book Review]

authors: Keegan, Te Taka Adrian Gregory

abstract: This article reviews the book “Matariki: The star of the year by Rangi Matamua", by Matamua, Rangi.

text: Matariki: The star of the year by Rangi Matamua [Book Review] This article reviews the book “Matariki: The star of the year by Rangi Matamua", by Matamua, Rangi.

year: 2017

title: Glycemic-aware metrics and oversampling techniques for predicting blood glucose levels using machine learning.

authors: Mayo, Michael

abstract: Techniques using machine learning for short term blood glucose level prediction in patients with Type 1 Diabetes are investigated. This problem is significant for the development of effective artificial pancreas technology so accurate alerts (e.g. hypoglycemia alarms) and other forecasts can be generated. It is shown that two factors must be considered when selecting the best machine learning technique for blood glucose level regression: (i) the regression model performance metrics being used to select the model, and (ii) the preprocessing techniques required to account for the imbalanced time spent by patients in different portions of the glycemic range. Using standard benchmark data, it is demonstrated that different regression model/preprocessing technique combinations exhibit different accuracies depending on the glycemic subrange under consideration. Therefore technique selection depends on the type of alert required. Specific findings are that a linear Support Vector Regression-based model, trained with normal as well as polynomial features, is best for blood glucose level forecasting in the normal and hyperglycemic ranges while a Multilayer Perceptron trained on oversampled data is ideal for predictions in the hypoglycemic range.

text: Glycemic-aware metrics and oversampling techniques for predicting blood glucose levels using machine learning. Techniques using machine learning for short term blood glucose level prediction in patients with Type 1 Diabetes are investigated. This problem is significant for the development of effective artificial pancreas technology so accurate alerts (e.g. hypoglycemia alarms) and other forecasts can be generated. It is shown that two factors must be considered when selecting the best machine learning technique for blood glucose level regression: (i) the regression model performance metrics being used to select the model, and (ii) the preprocessing techniques required to account for the imbalanced time spent by patients in different portions of the glycemic range. Using standard benchmark data, it is demonstrated that different regression model/preprocessing technique combinations exhibit different accuracies depending on the glycemic subrange under consideration. Therefore technique selection depends on the type of alert required. Specific findings are that a linear Support Vector Regression-based model, trained with normal as well as polynomial features, is best for blood glucose level forecasting in the normal and hyperglycemic ranges while a Multilayer Perceptron trained on oversampled data is ideal for predictions in the hypoglycemic range.

year: 2019