title: Globalisation, Citizenship and the War on Terror (Book Review)

authors: Smith, Ron C.

subjects: globalisation

abstract: The Editors of this book have put together a collection of post-modern musings by a clique of old-left academics from the University of Hull on the defects of contemporary democratic societies (like their own) and on the evils of globalisation.  They also provide a spirited defence of anti-western terrorism and terrorists; a project which is substantially sustained by a transparently self-serving use of both terms.  Purchase of the book is only recommended for citizens or parents who may wish to know what is being taught in departments of politics and sociology over much of the Western World.

text: Globalisation, Citizenship and the War on Terror (Book Review) The Editors of this book have put together a collection of post-modern musings by a clique of old-left academics from the University of Hull on the defects of contemporary democratic societies (like their own) and on the evils of globalisation.  They also provide a spirited defence of anti-western terrorism and terrorists; a project which is substantially sustained by a transparently self-serving use of both terms.  Purchase of the book is only recommended for citizens or parents who may wish to know what is being taught in departments of politics and sociology over much of the Western World.

year: 2008

title: Scalable Multi-label Classification

authors: Read, Jesse

subjects: multi-label

abstract: Multi-label classification is relevant to many domains, such as text, image and other media, and bioinformatics. Researchers have already noticed that in multi-label data, correlations exist between labels, and a variety of approaches, drawing inspiration from many spheres of machine learning, have been able to model these correlations. However, data sources from the real world are growing ever larger and the multi-label task is particularly sensitive to this due to the complexity associated with multiple labels and the correlations between them. Consequently, many methods do not scale up to large problems. This thesis deals with scalable multi-label classification: methods which exhibit high predictive performance, but are also able to scale up to larger problems. The first major contribution is the pruned sets method, which is able to model label correlations directly for high predictive performance, but reduces overfitting and complexity over related methods by pruning and subsampling label sets, and can thus scale up to larger datasets. The second major contribution is the classifier chains method, which models correlations with a chain of binary classifiers. The use of binary models allows for scalability to even larger datasets. Pruned sets and classifier chains are robust with respect to both the variety and scale of data that they can deal with, and can be incorporated into other methods. In an ensemble scheme, these methods are able to compete with state-of-the-art methods in terms of predictive performance as well as scale up to large datasets of hundreds of thousands of training examples. This thesis also puts a special emphasis on multi-label evaluation; introducing a new evaluation measure and studying threshold calibration. With one of the largest and most varied collections of multi-label datasets in the literature, extensive experimental evaluation shows the advantage of these methods, both in terms of predictive performance, and computational efficiency and scalability.

text: Scalable Multi-label Classification Multi-label classification is relevant to many domains, such as text, image and other media, and bioinformatics. Researchers have already noticed that in multi-label data, correlations exist between labels, and a variety of approaches, drawing inspiration from many spheres of machine learning, have been able to model these correlations. However, data sources from the real world are growing ever larger and the multi-label task is particularly sensitive to this due to the complexity associated with multiple labels and the correlations between them. Consequently, many methods do not scale up to large problems. This thesis deals with scalable multi-label classification: methods which exhibit high predictive performance, but are also able to scale up to larger problems. The first major contribution is the pruned sets method, which is able to model label correlations directly for high predictive performance, but reduces overfitting and complexity over related methods by pruning and subsampling label sets, and can thus scale up to larger datasets. The second major contribution is the classifier chains method, which models correlations with a chain of binary classifiers. The use of binary models allows for scalability to even larger datasets. Pruned sets and classifier chains are robust with respect to both the variety and scale of data that they can deal with, and can be incorporated into other methods. In an ensemble scheme, these methods are able to compete with state-of-the-art methods in terms of predictive performance as well as scale up to large datasets of hundreds of thousands of training examples. This thesis also puts a special emphasis on multi-label evaluation; introducing a new evaluation measure and studying threshold calibration. With one of the largest and most varied collections of multi-label datasets in the literature, extensive experimental evaluation shows the advantage of these methods, both in terms of predictive performance, and computational efficiency and scalability.

year: 2010

title: Kua Tupu Te Pā Harakeke: Developing Healthy Whānau Relationships

authors: Pihama, Leonie

abstract: We have opened this chapter with our ‘pēpeha’. Pēpeha are ways through which our people introduce ourselves. These vary from tribe to tribe. The essence of pēpeha is that they link us to our tribes and all associated with that. Our mountains, our rivers, our canoes, our ancestral lines. It links us to each other. It places us, as Māori, within a wider collective consciousness and relationships. It is important to highlight that the term ‘Māori’ is a term that brings us as tribal peoples together. It is a term that our people have chosen to use as a means of unifying ourselves in the wake of the arrival of our colonizers. Prior to colonization all identification was done through our whānau (extended family structure), hapū (subtribes) or iwi (tribes). The term Māori means to be ‘normal’ or ‘pure’, as such it is a fitting term for an Indigenous People. We have, however, also been active in maintaining our hapū and iwi identities and it is through pēpeha that we can culturally share that identity with each other. Our people have been doing this for generations. And in spite of colonization seeking to undermine our cultural identity, many of us continue to do this.

text: Kua Tupu Te Pā Harakeke: Developing Healthy Whānau Relationships We have opened this chapter with our ‘pēpeha’. Pēpeha are ways through which our people introduce ourselves. These vary from tribe to tribe. The essence of pēpeha is that they link us to our tribes and all associated with that. Our mountains, our rivers, our canoes, our ancestral lines. It links us to each other. It places us, as Māori, within a wider collective consciousness and relationships. It is important to highlight that the term ‘Māori’ is a term that brings us as tribal peoples together. It is a term that our people have chosen to use as a means of unifying ourselves in the wake of the arrival of our colonizers. Prior to colonization all identification was done through our whānau (extended family structure), hapū (subtribes) or iwi (tribes). The term Māori means to be ‘normal’ or ‘pure’, as such it is a fitting term for an Indigenous People. We have, however, also been active in maintaining our hapū and iwi identities and it is through pēpeha that we can culturally share that identity with each other. Our people have been doing this for generations. And in spite of colonization seeking to undermine our cultural identity, many of us continue to do this.

year: 2012