title: The production of machinima: A dialogue between ethnography, culture and space

authors: Schott, Gareth R.

subjects: videogame

abstract: Irrespective of the growing call for the legitimization and commercialization of machinima, as a mode of digital filmmaking using videogame hardware and software, this paper upholds an interest in the emergent play practices of gamers, rather than filmmakers, that embraces not only the curiosity and discovery exhibited within the fluid peripheries of the virtual worlds they inhabit, but also extends to delving into open-source 3D engines of games such as Half Life (Valve). It is this kind of playful exploration that was responsible for the initial emergence of machinima but also, we argue, for its continued and increasingly popular application as a key mode of articulating game fandom and the pleasures of play. Machinima is therefore examined here for its novel contribution to the existing multiple media channels adopted and utilized by participatory cultures that form around specific texts. Communities that are characterised not only by a shared practice (game playing) and heightened levels of appreciation, but also by the execution of creative practices that enunciate and define their specific ‘tastes’ whilst also transforming game texts to serve their alternative interests. Such practices are indicative of a contemporary fan-base that is no longer content with singular media experiences.

text: The production of machinima: A dialogue between ethnography, culture and space Irrespective of the growing call for the legitimization and commercialization of machinima, as a mode of digital filmmaking using videogame hardware and software, this paper upholds an interest in the emergent play practices of gamers, rather than filmmakers, that embraces not only the curiosity and discovery exhibited within the fluid peripheries of the virtual worlds they inhabit, but also extends to delving into open-source 3D engines of games such as Half Life (Valve). It is this kind of playful exploration that was responsible for the initial emergence of machinima but also, we argue, for its continued and increasingly popular application as a key mode of articulating game fandom and the pleasures of play. Machinima is therefore examined here for its novel contribution to the existing multiple media channels adopted and utilized by participatory cultures that form around specific texts. Communities that are characterised not only by a shared practice (game playing) and heightened levels of appreciation, but also by the execution of creative practices that enunciate and define their specific ‘tastes’ whilst also transforming game texts to serve their alternative interests. Such practices are indicative of a contemporary fan-base that is no longer content with singular media experiences.

year: 2011-07

title: The family trust in New Zealand and the claims of ‘Unwelcome Beneficiaries’

authors: Tappenden, Sue

subjects: Hayton

abstract: In June 2009, at the Transcontinental Trusts conference in Geneva, His Honour Justice David Hayton said that the New Zealand Court of Appeal had got aspects of the law of trusts wrong in its decision in Official Assignee v Wilson [2008] 3 NZLR 45. The Court held that the test for proving a trust was a sham was whether the trustees and settlor had a common intention that the trust was not to be a genuine entitiy. Hayton prefers a more objective approach and looks to the objective effect of a shammer’s conduct and not look for secret dishonest intentions which will hardly ever be revealed. Hayton’s approach would ensure that trust property would be made available to creditors so that they were paid what they were due by declaring the trusts to be shams. Family trusts have become big business in New Zealand and are commonly used to protect a businessman’s assets from creditors. While there is nothing illegal in setting up a family trust, it is my contention that the law pertaining to family trusts in New Zealand has become so far removed from the accepted principles of equity as to demand investigation.
 
This paper explores the origins of equity and compares the modern family trust against the equitable principles which have been developed over hundreds of years, even as far back as Plato’s Greece. The paper links the equity of Ancient Greece to Cicero in Rome, through the early Roman Church to the Chancellors serving English Kings. The law of England leads to the law of modern New Zealand. The paper goes on to examine the way the family trust has been used to defeat the legitimate claims of creditors. I aim to show that the approach taken by the New Zealand Court of Appeal is too narrow and favours the ‘shamming’ settlor at the expense of creditors who have given good consideration in comparison with volunteer beneficiaries. Moreover the family trust has become a mechanism that bears little relationship to recognised equitable principles and should lose the protection accorded to properly constituted trusts.

text: The family trust in New Zealand and the claims of ‘Unwelcome Beneficiaries’ In June 2009, at the Transcontinental Trusts conference in Geneva, His Honour Justice David Hayton said that the New Zealand Court of Appeal had got aspects of the law of trusts wrong in its decision in Official Assignee v Wilson [2008] 3 NZLR 45. The Court held that the test for proving a trust was a sham was whether the trustees and settlor had a common intention that the trust was not to be a genuine entitiy. Hayton prefers a more objective approach and looks to the objective effect of a shammer’s conduct and not look for secret dishonest intentions which will hardly ever be revealed. Hayton’s approach would ensure that trust property would be made available to creditors so that they were paid what they were due by declaring the trusts to be shams. Family trusts have become big business in New Zealand and are commonly used to protect a businessman’s assets from creditors. While there is nothing illegal in setting up a family trust, it is my contention that the law pertaining to family trusts in New Zealand has become so far removed from the accepted principles of equity as to demand investigation.
 
This paper explores the origins of equity and compares the modern family trust against the equitable principles which have been developed over hundreds of years, even as far back as Plato’s Greece. The paper links the equity of Ancient Greece to Cicero in Rome, through the early Roman Church to the Chancellors serving English Kings. The law of England leads to the law of modern New Zealand. The paper goes on to examine the way the family trust has been used to defeat the legitimate claims of creditors. I aim to show that the approach taken by the New Zealand Court of Appeal is too narrow and favours the ‘shamming’ settlor at the expense of creditors who have given good consideration in comparison with volunteer beneficiaries. Moreover the family trust has become a mechanism that bears little relationship to recognised equitable principles and should lose the protection accorded to properly constituted trusts.

year: 2009

title: Data and model bias in artificial intelligence for healthcare applications in New Zealand

authors: Yogarajan, Vithya

abstract: Introduction: Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand. Methods: This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME. Results: This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research. Discussions: This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

text: Data and model bias in artificial intelligence for healthcare applications in New Zealand Introduction: Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand. Methods: This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME. Results: This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research. Discussions: This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

year: 2022-12-22