title: An evaluation of the effectiveness of social equity strategies for Maori students in the School of Science and Technology

authors: Rua, Mohi

subjects: Maori students

abstract: In 1991 the School of Science and Technology (SOSAT) at the University of Waikato
had a very low participation rate of Maori and students from other non-dominant
ethnic groups. This situation was serious enough to concerned the then Dean of the
School and strategies were developed to change this situation. Four major strategies
are used to encourage, support and retain Maori students to successfully pursue and
complete a degree in Science. They are: the Te Putahi o te Manawa programme - a
mentoring programme; a scholarship and grant writing strategy (in particular
assistance with Tuapapa Putaiao Maori Fellowships(TPMFs) administered by FRST);
school visits; and field trips with secondary schools in the Waikato region that have a
high proportion of Maori students. The Maori & Psychology Research Unit (MPRU) was contracted by Gary Bramley of
the Equity Office of the SOSAT to conduct this evaluation. Evaluative information was gathered through administering questionnaires,
completing key informant interviews, and completing focus group interviews. In this evaluation we sought to determine the effectiveness of the social equity
strategies for Maori students in the SOSAT at the University of Waikato.

text: An evaluation of the effectiveness of social equity strategies for Maori students in the School of Science and Technology In 1991 the School of Science and Technology (SOSAT) at the University of Waikato
had a very low participation rate of Maori and students from other non-dominant
ethnic groups. This situation was serious enough to concerned the then Dean of the
School and strategies were developed to change this situation. Four major strategies
are used to encourage, support and retain Maori students to successfully pursue and
complete a degree in Science. They are: the Te Putahi o te Manawa programme - a
mentoring programme; a scholarship and grant writing strategy (in particular
assistance with Tuapapa Putaiao Maori Fellowships(TPMFs) administered by FRST);
school visits; and field trips with secondary schools in the Waikato region that have a
high proportion of Maori students. The Maori & Psychology Research Unit (MPRU) was contracted by Gary Bramley of
the Equity Office of the SOSAT to conduct this evaluation. Evaluative information was gathered through administering questionnaires,
completing key informant interviews, and completing focus group interviews. In this evaluation we sought to determine the effectiveness of the social equity
strategies for Maori students in the SOSAT at the University of Waikato.

year: 1999-11-11

title: Strawmen and the decolonisation of the academy

authors: Tauri, Juan Marcellus

subjects: Social justice

abstract: For this piece I take as my point of departure Biko Agozino’s response to a series of questions posed by a member of the audience, to a panel he was a member of during this time in New Zealand. The panel in question was part of the launch of a new journal, Decolonisation of Criminology and Justice, at AUT University, Auckland in June 2018. Biko was invited to give a speech at the launch of the journal, and participate in a panel discussion on the broad theme of ‘decolonising criminology and criminal justice’.

text: Strawmen and the decolonisation of the academy For this piece I take as my point of departure Biko Agozino’s response to a series of questions posed by a member of the audience, to a panel he was a member of during this time in New Zealand. The panel in question was part of the launch of a new journal, Decolonisation of Criminology and Justice, at AUT University, Auckland in June 2018. Biko was invited to give a speech at the launch of the journal, and participate in a panel discussion on the broad theme of ‘decolonising criminology and criminal justice’.

year: 2019-07-10

title: Scalable and efficient multi-label classification for evolving data streams

authors: Read, Jesse

subjects: Machine learning

abstract: Many challenging real world problems involve multi-label data streams. Efficient methods exist for multi-label classification in non-streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as classifiers must be able to deal with huge numbers of examples and to adapt to change using limited time and memory while being ready to predict at any point.
This paper proposes a new experimental framework for learning and evaluating on multi-label data streams, and uses it to study the performance of various methods. From this study, we develop a multi-label Hoeffding tree with multi-label classifiers at the leaves. We show empirically that this method is well suited to this challenging task. Using our new framework, which allows us to generate realistic multi-label data streams with concept drift (as well as real data), we compare with a selection of baseline methods, as well as new learning methods from the literature, and show that our Hoeffding tree method achieves fast and more accurate performance.

text: Scalable and efficient multi-label classification for evolving data streams Many challenging real world problems involve multi-label data streams. Efficient methods exist for multi-label classification in non-streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as classifiers must be able to deal with huge numbers of examples and to adapt to change using limited time and memory while being ready to predict at any point.
This paper proposes a new experimental framework for learning and evaluating on multi-label data streams, and uses it to study the performance of various methods. From this study, we develop a multi-label Hoeffding tree with multi-label classifiers at the leaves. We show empirically that this method is well suited to this challenging task. Using our new framework, which allows us to generate realistic multi-label data streams with concept drift (as well as real data), we compare with a selection of baseline methods, as well as new learning methods from the literature, and show that our Hoeffding tree method achieves fast and more accurate performance.

year: 2012