title: Machine learning in practice: experience with agricultural databases

authors: Garner, Stephen R.

subjects: computer science

abstract: The Waikato Environment for Knowledge Analysis (weka) is a New Zealand government-sponsored initiative to investigate the application of machine learning to economically important problems in the agricultural industries. The overall goals are to create a workbench for machine learning, determine the factors that contribute towards its successful application in the agricultural industries, and develop new methods of machine learning and ways of assessing their effectiveness. The project began in 1993 and is currently working towards the fulfilment of three objectives: to design and implement the workbench, to provide case studies of applications of machine learning techniques to problems in agriculture, and to develop a methodology for evaluating generalisations in terms of their entropy. These three objectives are by no means independent. For example, the design of the weka workbench has been inspired by the demands placed on it by the case studies, and has also benefited from our work on evaluating the outcomes of applying a technique to data. Our experience throughout the development of the project is that the successful application of machine learning involves much more than merely executing a learning algorithm on some data. In this paper we present the process model that underpins our work over the past two years for the development of applications in agriculture; the software we have developed around our workbench of machine learning schemes to support this model; and the outcomes and problems we have encountered in developing applications.

text: Machine learning in practice: experience with agricultural databases The Waikato Environment for Knowledge Analysis (weka) is a New Zealand government-sponsored initiative to investigate the application of machine learning to economically important problems in the agricultural industries. The overall goals are to create a workbench for machine learning, determine the factors that contribute towards its successful application in the agricultural industries, and develop new methods of machine learning and ways of assessing their effectiveness. The project began in 1993 and is currently working towards the fulfilment of three objectives: to design and implement the workbench, to provide case studies of applications of machine learning techniques to problems in agriculture, and to develop a methodology for evaluating generalisations in terms of their entropy. These three objectives are by no means independent. For example, the design of the weka workbench has been inspired by the demands placed on it by the case studies, and has also benefited from our work on evaluating the outcomes of applying a technique to data. Our experience throughout the development of the project is that the successful application of machine learning involves much more than merely executing a learning algorithm on some data. In this paper we present the process model that underpins our work over the past two years for the development of applications in agriculture; the software we have developed around our workbench of machine learning schemes to support this model; and the outcomes and problems we have encountered in developing applications.

year: 1995-05

title: Latent Transition Analysis indicates four relatively stable profiles of loneliness in New Zealand

authors: Hammond, Matthew D.

subjects: Science & Technology

abstract: Objective: We investigated the characteristics of loneliness by identifying distinct ‘profiles’ of loneliness and investigating transitions between those loneliness profiles over two years. Method: We conducted Latent Transition Analyses on two years of data from the New Zealand Attitudes and Values Study (N=15,820) and modelled how people’s health and age were associated with changes in profile membership. Results: Four loneliness profiles emerged: ‘low-loneliness’ (58% of the sample), ‘high-loneliness’ (5%), ‘appreciated outsiders’ (28%; perceived acceptance from others but felt like social outsiders), and ‘superficially connected’ (9%; lacked acceptance from others but felt socially included). Profile membership was relatively stable over time and transitions were most likely from higher to lower loneliness. Younger people and people reporting poorer health were more likely to transition into profiles with greater loneliness indicators. Conclusions: Findings replicated a four-profile pattern of loneliness, supported the theorised ‘trait-like’ structure of loneliness and identified the possibility that moderate states of loneliness are transitional states into/from low and high loneliness. Implications for public health: The stability of loneliness across years reiterates the need for societal interventions, particularly interventions that are adaptive to whether people’s loneliness forms as a lack of acceptance/value or a lack of social inclusion.

text: Latent Transition Analysis indicates four relatively stable profiles of loneliness in New Zealand Objective: We investigated the characteristics of loneliness by identifying distinct ‘profiles’ of loneliness and investigating transitions between those loneliness profiles over two years. Method: We conducted Latent Transition Analyses on two years of data from the New Zealand Attitudes and Values Study (N=15,820) and modelled how people’s health and age were associated with changes in profile membership. Results: Four loneliness profiles emerged: ‘low-loneliness’ (58% of the sample), ‘high-loneliness’ (5%), ‘appreciated outsiders’ (28%; perceived acceptance from others but felt like social outsiders), and ‘superficially connected’ (9%; lacked acceptance from others but felt socially included). Profile membership was relatively stable over time and transitions were most likely from higher to lower loneliness. Younger people and people reporting poorer health were more likely to transition into profiles with greater loneliness indicators. Conclusions: Findings replicated a four-profile pattern of loneliness, supported the theorised ‘trait-like’ structure of loneliness and identified the possibility that moderate states of loneliness are transitional states into/from low and high loneliness. Implications for public health: The stability of loneliness across years reiterates the need for societal interventions, particularly interventions that are adaptive to whether people’s loneliness forms as a lack of acceptance/value or a lack of social inclusion.

year: 2022-05-26

title: Sharing, listening, learning and developing understandings of Kaupapa Māori research by engaging with two Māori communities involved in education.

authors: Kana, Fred

subjects: Kaupapa Maori research methodology

abstract: This paper is a culmination of common understandings that were elicited from two pieces of research: ‘The Impact of the BHP New Zealand Steel Mining on the Tangata Whenua and the Environment’ and ‘The Impact of Maori Medium Education within a Mainstream Secondary School on the Lives of its Participants, in particular the Teachers, Caregivers and Students’. It was at the conclusion of each research project and as a consequence of informal conversations and discussions that this paper evolved. The paper discusses shared understandings in the context of Kaupapa Māori research methodology and key findings from the two research projects.

text: Sharing, listening, learning and developing understandings of Kaupapa Māori research by engaging with two Māori communities involved in education. This paper is a culmination of common understandings that were elicited from two pieces of research: ‘The Impact of the BHP New Zealand Steel Mining on the Tangata Whenua and the Environment’ and ‘The Impact of Maori Medium Education within a Mainstream Secondary School on the Lives of its Participants, in particular the Teachers, Caregivers and Students’. It was at the conclusion of each research project and as a consequence of informal conversations and discussions that this paper evolved. The paper discusses shared understandings in the context of Kaupapa Māori research methodology and key findings from the two research projects.

year: 2006