title: The development impact of a best practice seasonal worker policy: New Zealand’s Recognised Seasonal Employer (RSE) Scheme

authors: Gibson, John

subjects: seasonal migration

abstract: Seasonal migration programs are widely used around the world, and are increasingly seen as offering a potential “triple-win”- benefiting the migrant, sending country, and receiving country. Yet there is a dearth of rigorous evidence as to their development impact, and concerns about whether the time periods involved are too short to realize much in the way of benefits, and whether poorer, less skilled households actually get to participate in such programs. We study the development impacts of a recently introduced seasonal worker program which has been deemed to be “best practice”. New Zealand’s Recognised Seasonal Employer (RSE) program was launched in 2007 with an explicit focus on development in the Pacific alongside the aim of benefiting employers at home. A multi-year prospective evaluation allows us to measure the impact of participation in this program on households and communities in Tonga and Vanuatu. Using a matched difference-in-differences analysis based on detailed surveys fielded before, during, and after participation, we find that the RSE has indeed had largely positive development impacts. It has increased income and consumption of households, allowed households to purchase more durable goods, increased subjective standard of living, and had additional benefits at the community level. It also increased child schooling in Tonga. This should rank it among the most effective development policies evaluated to date. The policy was designed as a best practice example based on lessons elsewhere, and now should serve as a model for other countries to follow.

text: The development impact of a best practice seasonal worker policy: New Zealand’s Recognised Seasonal Employer (RSE) Scheme Seasonal migration programs are widely used around the world, and are increasingly seen as offering a potential “triple-win”- benefiting the migrant, sending country, and receiving country. Yet there is a dearth of rigorous evidence as to their development impact, and concerns about whether the time periods involved are too short to realize much in the way of benefits, and whether poorer, less skilled households actually get to participate in such programs. We study the development impacts of a recently introduced seasonal worker program which has been deemed to be “best practice”. New Zealand’s Recognised Seasonal Employer (RSE) program was launched in 2007 with an explicit focus on development in the Pacific alongside the aim of benefiting employers at home. A multi-year prospective evaluation allows us to measure the impact of participation in this program on households and communities in Tonga and Vanuatu. Using a matched difference-in-differences analysis based on detailed surveys fielded before, during, and after participation, we find that the RSE has indeed had largely positive development impacts. It has increased income and consumption of households, allowed households to purchase more durable goods, increased subjective standard of living, and had additional benefits at the community level. It also increased child schooling in Tonga. This should rank it among the most effective development policies evaluated to date. The policy was designed as a best practice example based on lessons elsewhere, and now should serve as a model for other countries to follow.

year: 2010-11

title: Effective classifiers for detecting objects

authors: Mayo, Michael

subjects: computer science

abstract: Several state-of-the-art machine learning classifiers are compared for the purposes of object detection in complex images, using global image features derived from the Ohta color space and Local Binary Patterns. Image complexity in this sense refers to the degree to which the target objects are occluded and/or non-dominant (i.e. not in the foreground) in the image, and also the degree to which the images are cluttered with non-target objects. The results indicate that a voting ensemble of Support Vector Machines, Random Forests, and Boosted Decision Trees provide the best performance with AUC values of up to 0.92 and Equal Error Rate accuracies of up to 85.7% in stratified 10-fold cross validation experiments on the GRAZ02 complex image dataset.

text: Effective classifiers for detecting objects Several state-of-the-art machine learning classifiers are compared for the purposes of object detection in complex images, using global image features derived from the Ohta color space and Local Binary Patterns. Image complexity in this sense refers to the degree to which the target objects are occluded and/or non-dominant (i.e. not in the foreground) in the image, and also the degree to which the images are cluttered with non-target objects. The results indicate that a voting ensemble of Support Vector Machines, Random Forests, and Boosted Decision Trees provide the best performance with AUC values of up to 0.92 and Equal Error Rate accuracies of up to 85.7% in stratified 10-fold cross validation experiments on the GRAZ02 complex image dataset.

year: 2007

title: Te Kotahitanga: culturally responsive professional development for teachers

authors: Bishop, Russell

subjects: indigenous

abstract: Te Kotahitanga is a research and professional development project that aims to support teachers to raise the achievement of New Zealand's indigenous Māori students in public/mainstream classrooms. An Effective Teaching Profile, developed from the voices of Māori students, their families, principals and some of their teachers, provides direction and focus for both the classroom pedagogy and the professional development. While the authors understand that there are many institutional changes necessary at the school level, this paper focuses on the professional learning opportunities developed for classroom teachers within this project to support the development of more effective classroom relationships and interactions with Māori students. This has resulted in Māori students attending school more regularly, engaging as learners and achieving to levels where they begin to realise their true potential.

text: Te Kotahitanga: culturally responsive professional development for teachers Te Kotahitanga is a research and professional development project that aims to support teachers to raise the achievement of New Zealand's indigenous Māori students in public/mainstream classrooms. An Effective Teaching Profile, developed from the voices of Māori students, their families, principals and some of their teachers, provides direction and focus for both the classroom pedagogy and the professional development. While the authors understand that there are many institutional changes necessary at the school level, this paper focuses on the professional learning opportunities developed for classroom teachers within this project to support the development of more effective classroom relationships and interactions with Māori students. This has resulted in Māori students attending school more regularly, engaging as learners and achieving to levels where they begin to realise their true potential.

year: 2010-05