title: Reproducing the general through the local: Lessons from poverty research

authors: Hodgetts, Darrin

subjects: Generalisation

abstract: Central to research into the conduct of everyday life are issues of generalisation. This chapter focuses on three interrelated forms or manifestations of generalisation, which invoke issues around how macro-level structures and intergroup relations are reproduced through micro-level situations. First, theoretical generalisation constitutes our efforts to enlarge the significance of small-scale exemplars through research by relating local insights to the broader body of academic knowledge. Second, referential generalisation involves relating everyday artefacts produced by our research participants to the broader social context and intergroup relations at play. Third, empathetic generalisation involves promoting witnessing, recognition, and empathy towards people experiencing poverty by people who are not living in poverty. These three forms or elements of generalisation are central to the development of action strategies to address issues of poverty.

text: Reproducing the general through the local: Lessons from poverty research Central to research into the conduct of everyday life are issues of generalisation. This chapter focuses on three interrelated forms or manifestations of generalisation, which invoke issues around how macro-level structures and intergroup relations are reproduced through micro-level situations. First, theoretical generalisation constitutes our efforts to enlarge the significance of small-scale exemplars through research by relating local insights to the broader body of academic knowledge. Second, referential generalisation involves relating everyday artefacts produced by our research participants to the broader social context and intergroup relations at play. Third, empathetic generalisation involves promoting witnessing, recognition, and empathy towards people experiencing poverty by people who are not living in poverty. These three forms or elements of generalisation are central to the development of action strategies to address issues of poverty.

year: 2019

title: Real time analysis of cataract surgery videos using deep learning

authors: Whitten, Jesse

subjects: Deep learning

abstract: A deep learning framework is proposed that classifies surgical instruments within cataract surgery videos and generates clinical summary reports, proving objective reporting and analysis. This framework is run in real time on an embedded system, potentially allowing real time feedback directly in a clinical environment. To evaluate this framework, a novel dataset of 39 cataract surgery videos is sampled at different frame rates and labeled with 10 surgical instrument classes. This dataset is used to train and evaluate several image classification deep learning model architectures: ResNet-50, ResNet-152 and InceptionV3. An Nvidia Jetson Nano 4GB embedded system is used to operate an optimised variation of the framework, evaluating the accuracy and representation of clinical summary reports. It was found that the deep learning models provided a maximum mean predictive AUC score of 0.982 and a sensitivity of 0.968 on hold out test videos. The Jetson Nano was demonstrated to provide accurate and representative clinical summary reports with a video frame rate of 6 frames per second, completing in real time. These results show that the deep learning framework is viable for integration into the clinical environment, automatically providing clinical summary reports containing objective assessment statistics, video annotations and graphical timeline visualisations of cataract removal surgeries.

text: Real time analysis of cataract surgery videos using deep learning A deep learning framework is proposed that classifies surgical instruments within cataract surgery videos and generates clinical summary reports, proving objective reporting and analysis. This framework is run in real time on an embedded system, potentially allowing real time feedback directly in a clinical environment. To evaluate this framework, a novel dataset of 39 cataract surgery videos is sampled at different frame rates and labeled with 10 surgical instrument classes. This dataset is used to train and evaluate several image classification deep learning model architectures: ResNet-50, ResNet-152 and InceptionV3. An Nvidia Jetson Nano 4GB embedded system is used to operate an optimised variation of the framework, evaluating the accuracy and representation of clinical summary reports. It was found that the deep learning models provided a maximum mean predictive AUC score of 0.982 and a sensitivity of 0.968 on hold out test videos. The Jetson Nano was demonstrated to provide accurate and representative clinical summary reports with a video frame rate of 6 frames per second, completing in real time. These results show that the deep learning framework is viable for integration into the clinical environment, automatically providing clinical summary reports containing objective assessment statistics, video annotations and graphical timeline visualisations of cataract removal surgeries.

year: 2022

title: Introducing the Draft Learning Progression Framework for Maori in the New Zealand Curriculum

authors: Crombie, Winifred

subjects: Māori education

abstract: This paper introduces the draft Learning Progression Framework for Māori in mainstream schools which we prepared in the first half of 2003 for the New Zealand Ministry of Education. Readers are invited to contact the authors if they wish to make any suggestions in relation to the final content of the document.

text: Introducing the Draft Learning Progression Framework for Maori in the New Zealand Curriculum This paper introduces the draft Learning Progression Framework for Māori in mainstream schools which we prepared in the first half of 2003 for the New Zealand Ministry of Education. Readers are invited to contact the authors if they wish to make any suggestions in relation to the final content of the document.

year: 2003-09