title: Understanding ‘higher’ Māori fertility in a ‘low’ fertility context: Does cultural identity make a difference?

authors: Rarere, Moana

subjects: Maori fertility

abstract: The Māori fertility transition brought an end to decades of very high fertility rates, and a convergence towards long-term fertility levels similar to Pākehā/New Zealand European women. However, important differences endure. The Māori total fertility rate (TFR) remains above replacement level, and Māori women have children earlier and over a longer period. All of this has and still is occurring in a society that facilitates and favours low fertility and small family sizes. Using births data and cultural identity markers in the New Zealand Census, this paper explores the influence of culture as a contributing factor to higher fertility outcomes amongst Māori women in a low-fertility society.

text: Understanding ‘higher’ Māori fertility in a ‘low’ fertility context: Does cultural identity make a difference? The Māori fertility transition brought an end to decades of very high fertility rates, and a convergence towards long-term fertility levels similar to Pākehā/New Zealand European women. However, important differences endure. The Māori total fertility rate (TFR) remains above replacement level, and Māori women have children earlier and over a longer period. All of this has and still is occurring in a society that facilitates and favours low fertility and small family sizes. Using births data and cultural identity markers in the New Zealand Census, this paper explores the influence of culture as a contributing factor to higher fertility outcomes amongst Māori women in a low-fertility society.

year: 2018

title: Microsoft Translator Hub for Māori language

authors: Keegan, Te Taka Adrian Gregory

subjects: computer science

abstract: Recent improvements in Machine Translation (MT) software has opened new possibilities for applications of automatic language translation. But can these opportunities exist for the smaller, minority languages of the world? Training data was collected for the language pair of Māori and English which was used to build an MT system using Microsoft’s Translator Hub software. A comparative analysis was undertaken with this system and Google Translate. Various MT metrics and analysis software was considered before deciding to use the Asiya toolkit to undertake the comparative analysis. Māori language experts were also used to provide a human perspective on the output translations. The overall results showed no significant difference in the translation quality produced by the two systems. Despite some misgivings around the accuracy of the translations these results do show promise for MT usage by minority languages.

text: Microsoft Translator Hub for Māori language Recent improvements in Machine Translation (MT) software has opened new possibilities for applications of automatic language translation. But can these opportunities exist for the smaller, minority languages of the world? Training data was collected for the language pair of Māori and English which was used to build an MT system using Microsoft’s Translator Hub software. A comparative analysis was undertaken with this system and Google Translate. Various MT metrics and analysis software was considered before deciding to use the Asiya toolkit to undertake the comparative analysis. Māori language experts were also used to provide a human perspective on the output translations. The overall results showed no significant difference in the translation quality produced by the two systems. Despite some misgivings around the accuracy of the translations these results do show promise for MT usage by minority languages.

year: 2018

title: Scientific workflow management with ADAMS: building and data mining a database of crop protection and related data

authors: Reutemann, Peter

subjects: Machine learning

abstract: Data mining is said to be a field that encourages data to speak for itself rather than “forcing” data to conform to a pre-specified model, but we have to acknowledge that what is spoken by the data may well be gibberish. To obtain meaning from data it is important to use techniques systematically, to follow sound experimental procedure and to examine results expertly. This paper presents a framework for scientific discovery from data with two examples from the biological sciences. The first case is a re-investigation of previously published work on aphid trap data to predict aphid phenology and the second is a commercial application for identifying and counting insects captured on sticky plates in greenhouses. Using support vector machines rather than neural networks or linear regression gives better results in case of the aphid trap data. For both cases, we use the open source machine learning workbench WEKA for predictive modelling and the open source ADAMS workflow system for automating data collection, preparation, feature generation, application of predictive models and output generation.

text: Scientific workflow management with ADAMS: building and data mining a database of crop protection and related data Data mining is said to be a field that encourages data to speak for itself rather than “forcing” data to conform to a pre-specified model, but we have to acknowledge that what is spoken by the data may well be gibberish. To obtain meaning from data it is important to use techniques systematically, to follow sound experimental procedure and to examine results expertly. This paper presents a framework for scientific discovery from data with two examples from the biological sciences. The first case is a re-investigation of previously published work on aphid trap data to predict aphid phenology and the second is a commercial application for identifying and counting insects captured on sticky plates in greenhouses. Using support vector machines rather than neural networks or linear regression gives better results in case of the aphid trap data. For both cases, we use the open source machine learning workbench WEKA for predictive modelling and the open source ADAMS workflow system for automating data collection, preparation, feature generation, application of predictive models and output generation.

year: 2015