title: Social music in cars

authors: Cunningham, Sally Jo

abstract: This paper builds an understanding of how music is currently experienced by a social group travelling together in a car - how songs are chosen for playing, how music both reflects and influences the group’s mood and social interaction, who supplies the music, the hardware/software that supports song selection and presentation. This fine-grained context emerges from a qualitative analysis of a rich set of ethnographic data (participant observations and interviews) focusing primarily on the experience of in-car music on moderate length and long trips. We suggest features and functionality for music software to enhance the social experience when travelling in cars, and prototype and test a user interface based on design suggestions drawn from the data.

text: Social music in cars This paper builds an understanding of how music is currently experienced by a social group travelling together in a car - how songs are chosen for playing, how music both reflects and influences the group’s mood and social interaction, who supplies the music, the hardware/software that supports song selection and presentation. This fine-grained context emerges from a qualitative analysis of a rich set of ethnographic data (participant observations and interviews) focusing primarily on the experience of in-car music on moderate length and long trips. We suggest features and functionality for music software to enhance the social experience when travelling in cars, and prototype and test a user interface based on design suggestions drawn from the data.

year: 2014

title: Eliciting Expert Uncertainty from Decision Making

authors: Falconer, Julia Ruth

subjects: bayesian

abstract: Eliciting expert uncertainty is a complex task with many caveats. Its usefulness in high-level decision-making and Bayesian inference makes it a necessary task that must be completed accurately. This thesis explores uncertainty elicitation in terms of prior elicitation for Bayesian inference and the wider knowledge elicitation  field. It describes methods currently available for prior elicitation and proposes a new typology, highlighting lesser-known methods. Based on the limitations of current methods, a new method for prior elicitation is introduced. This new method allows an analyst to model expert decision-making data to elicit a probability distribution that reflects expert uncertainty. An example of parole board decision-making is used to elicit a prior distribution of a prisoner re-offending upon release from prison. This example shows analysts how to elicit distributions for tabular data; however, more complex data types, such as images and reports, are often used for decision-making. To elicit distributions capturing expert uncertainty from more complex data, this thesis introduces a deep learning approach and uses an example of eliciting cancer risk, in histopathology, to illustrate this approach for the wider knowledge elicitation field.

text: Eliciting Expert Uncertainty from Decision Making Eliciting expert uncertainty is a complex task with many caveats. Its usefulness in high-level decision-making and Bayesian inference makes it a necessary task that must be completed accurately. This thesis explores uncertainty elicitation in terms of prior elicitation for Bayesian inference and the wider knowledge elicitation  field. It describes methods currently available for prior elicitation and proposes a new typology, highlighting lesser-known methods. Based on the limitations of current methods, a new method for prior elicitation is introduced. This new method allows an analyst to model expert decision-making data to elicit a probability distribution that reflects expert uncertainty. An example of parole board decision-making is used to elicit a prior distribution of a prisoner re-offending upon release from prison. This example shows analysts how to elicit distributions for tabular data; however, more complex data types, such as images and reports, are often used for decision-making. To elicit distributions capturing expert uncertainty from more complex data, this thesis introduces a deep learning approach and uses an example of eliciting cancer risk, in histopathology, to illustrate this approach for the wider knowledge elicitation field.

year: 2024

title: Challenges to legal education: The Waikato Law School experience

authors: Wilson, Margaret

subjects: legal education

abstract: The experiences of the Waikato Law School especially from the viewpoint of challenges to legal education are discussed. The impact of the performance-based model of funding on the delivery of legal education at the Waikato Law School is highlighted.

text: Challenges to legal education: The Waikato Law School experience The experiences of the Waikato Law School especially from the viewpoint of challenges to legal education are discussed. The impact of the performance-based model of funding on the delivery of legal education at the Waikato Law School is highlighted.

year: 2010