title: Activating whanau (extended family) processes within a community and school literacy partnership

authors: Glynn, Ted

subjects: Maori Education

abstract: "The paper describes the positive outcomes of a home and school literacy partnership in one mainstream primary school where the principal and the large majority of students, teachers, and whānau belong to one Māori hapū (sub-tribe), Ngāti Whakaue. Participating students were identified by teachers as experiencing difficulties with reading and writing. They were assigned randomly to one of two conditions: (1) participating in their school’s regular reading programme (school group) or (2) participating also in the home tutored reading and writing programme (home and school group).

Students in the home and school group improved their reading and writing outcomes over and above the outcomes achieved by students participating only in the regular school programme. There were two key reasons for the success of this partnership. The first was parents and whānau members successful implementation of both a Māori language reading tutoring programme, Tatari, Tautoko Tauawhi, and the two components of the writing programme, Tuhi Atu Tui Mai and Whakaputa Whakaaro. The second was the professional educational and cultural expertise of the home and school liaison worker, Hiro. Because of her language and cultural expertise and her mana (acknowledged authority and standing), Hiro was able to engage Māori parents and whānau actively in the home and school project. The co-constructed narrative between Hiro and Mere (the second author) provided a salient context for understanding the power of culturally preferred pedagogy in establishing effective home and school partnerships."

text: Activating whanau (extended family) processes within a community and school literacy partnership "The paper describes the positive outcomes of a home and school literacy partnership in one mainstream primary school where the principal and the large majority of students, teachers, and whānau belong to one Māori hapū (sub-tribe), Ngāti Whakaue. Participating students were identified by teachers as experiencing difficulties with reading and writing. They were assigned randomly to one of two conditions: (1) participating in their school’s regular reading programme (school group) or (2) participating also in the home tutored reading and writing programme (home and school group).

Students in the home and school group improved their reading and writing outcomes over and above the outcomes achieved by students participating only in the regular school programme. There were two key reasons for the success of this partnership. The first was parents and whānau members successful implementation of both a Māori language reading tutoring programme, Tatari, Tautoko Tauawhi, and the two components of the writing programme, Tuhi Atu Tui Mai and Whakaputa Whakaaro. The second was the professional educational and cultural expertise of the home and school liaison worker, Hiro. Because of her language and cultural expertise and her mana (acknowledged authority and standing), Hiro was able to engage Māori parents and whānau actively in the home and school project. The co-constructed narrative between Hiro and Mere (the second author) provided a salient context for understanding the power of culturally preferred pedagogy in establishing effective home and school partnerships."

year: 2004

title: Feminist geographies in Aotearoa New Zealand: cultural, social and political moments

authors: Adams-Hutcheson, Gail

subjects: Social Sciences

abstract: Aotearoa New Zealand is a nation of promise, potential and enigma: it was the first country in the world where women gained the vote in 1893 and now boasts the youngest woman world leader in 2017. It is also a postcolonial nation where structural racism, homophobia, and sexism persist, yet it has also given legal personhood to a river. Our Country Report foregrounds Aotearoa New Zealand feminist geographic scholarship that responds to, reflects, and sometimes resists such contrasts and contradictions at the national scale. We employ the lens of the 2017 national election to critically engage with current gendered and indigenous politics in the country. Analyzing these politics through three ‘feminist moments,’ our paper highlights the breadth and scope of current Aotearoa New Zealand feminist geographic scholarship and directions.

text: Feminist geographies in Aotearoa New Zealand: cultural, social and political moments Aotearoa New Zealand is a nation of promise, potential and enigma: it was the first country in the world where women gained the vote in 1893 and now boasts the youngest woman world leader in 2017. It is also a postcolonial nation where structural racism, homophobia, and sexism persist, yet it has also given legal personhood to a river. Our Country Report foregrounds Aotearoa New Zealand feminist geographic scholarship that responds to, reflects, and sometimes resists such contrasts and contradictions at the national scale. We employ the lens of the 2017 national election to critically engage with current gendered and indigenous politics in the country. Analyzing these politics through three ‘feminist moments,’ our paper highlights the breadth and scope of current Aotearoa New Zealand feminist geographic scholarship and directions.

year: 2019

title: Propositionalisation of multi-instance data using random forests

authors: Frank, Eibe

subjects: Machine learning

abstract: Multi-instance learning is a generalisation of attribute-value learning where examples for learning consist of labeled bags (i.e. multi-sets) of instances. This learning setting is more computationally challenging than attribute-value learning and a natural fit for important application areas of machine learning such as classification of molecules and image classification. One approach to solve multi-instance learning problems is to apply propositionalisation, where bags of data are converted into vectors of attribute-value pairs so that a standard propositional (i.e. attribute-value) learning algorithm can be applied. This approach is attractive because of the large number of propositional learning algorithms that have been developed and can thus be applied to the propositionalised data. In this paper, we empirically investigate a variant of an existing propositionalisation method called TLC. TLC uses a single decision tree to obtain propositionalised data. Our variant applies a random forest instead and is motivated by the potential increase in robustness that this may yield. We present results on synthetic and real-world data from the above two application domains showing that it indeed yields increased classification accuracy when applying boosting and support vector machines to classify the propositionalised data.

text: Propositionalisation of multi-instance data using random forests Multi-instance learning is a generalisation of attribute-value learning where examples for learning consist of labeled bags (i.e. multi-sets) of instances. This learning setting is more computationally challenging than attribute-value learning and a natural fit for important application areas of machine learning such as classification of molecules and image classification. One approach to solve multi-instance learning problems is to apply propositionalisation, where bags of data are converted into vectors of attribute-value pairs so that a standard propositional (i.e. attribute-value) learning algorithm can be applied. This approach is attractive because of the large number of propositional learning algorithms that have been developed and can thus be applied to the propositionalised data. In this paper, we empirically investigate a variant of an existing propositionalisation method called TLC. TLC uses a single decision tree to obtain propositionalised data. Our variant applies a random forest instead and is motivated by the potential increase in robustness that this may yield. We present results on synthetic and real-world data from the above two application domains showing that it indeed yields increased classification accuracy when applying boosting and support vector machines to classify the propositionalised data.

year: 2013