title: Expanding the agenda for research on the physically active aging body

authors: Phoenix, Cassandra

subjects: physical activity

abstract: In this article, the authors consider the different approaches that can be used to examine the relationship between physical activity and aging. They propose that much is to be gained in our awareness of this dynamic relationship by drawing on multiple forms of knowledge that can generate diverse understandings regarding the impact of physical activity on physiological, psychological, and social aspects of aging. Accordingly, 3 different approaches to understanding the older physically (in)active body are presented. These are categorized as (a) the objective truth about the aging, physically active body; (b) the subjective truth about the aging, physically active body; and (c) “tales” about the aging, physically active body. The key underpinnings, strengths, and weaknesses of each approach are outlined. A number of examples from the literature are also offered to demonstrate where and how each approach has been used to contribute to our understanding about older people and physical activity. The more thorough, multidisciplinary, and wide spanning our knowledge of the aging, active body is, the more informed we might become in every dimension of its existence.

text: Expanding the agenda for research on the physically active aging body In this article, the authors consider the different approaches that can be used to examine the relationship between physical activity and aging. They propose that much is to be gained in our awareness of this dynamic relationship by drawing on multiple forms of knowledge that can generate diverse understandings regarding the impact of physical activity on physiological, psychological, and social aspects of aging. Accordingly, 3 different approaches to understanding the older physically (in)active body are presented. These are categorized as (a) the objective truth about the aging, physically active body; (b) the subjective truth about the aging, physically active body; and (c) “tales” about the aging, physically active body. The key underpinnings, strengths, and weaknesses of each approach are outlined. A number of examples from the literature are also offered to demonstrate where and how each approach has been used to contribute to our understanding about older people and physical activity. The more thorough, multidisciplinary, and wide spanning our knowledge of the aging, active body is, the more informed we might become in every dimension of its existence.

year: 2009

title: A comparison of machine learning methods for cross-domain few-shot learning

authors: Wang, Hongyu

subjects: computer science

abstract: We present an empirical evaluation of machine learning algorithms in cross-domain few-shot learning based on a fixed pre-trained feature extractor. Experiments were performed in five target domains (CropDisease, EuroSAT, Food101, ISIC and ChestX) and using two feature extractors: a ResNet10 model trained on a subset of ImageNet known as miniImageNet and a ResNet152 model trained on the ILSVRC 2012 subset of ImageNet. Commonly used machine learning algorithms including logistic regression, support vector machines, random forests, nearest neighbour classification, naïve Bayes, and linear and quadratic discriminant analysis were evaluated on the extracted feature vectors. We also evaluated classification accuracy when subjecting the feature vectors to normalisation using p-norms. Algorithms originally developed for the classification of gene expression data—the nearest shrunken centroid algorithm and LDA ensembles obtained with random projections—were also included in the experiments, in addition to a cosine similarity classifier that has recently proved popular in few-shot learning. The results enable us to identify algorithms, normalisation methods and pre-trained feature extractors that perform well in cross-domain few-shot learning. We show that the cosine similarity classifier and   ℓ² -regularised 1-vs-rest logistic regression are generally the best-performing algorithms. We also show that algorithms such as LDA yield consistently higher accuracy when applied to   ℓ² -normalised feature vectors. In addition, all classifiers generally perform better when extracting feature vectors using the ResNet152 model instead of the ResNet10 model.

text: A comparison of machine learning methods for cross-domain few-shot learning We present an empirical evaluation of machine learning algorithms in cross-domain few-shot learning based on a fixed pre-trained feature extractor. Experiments were performed in five target domains (CropDisease, EuroSAT, Food101, ISIC and ChestX) and using two feature extractors: a ResNet10 model trained on a subset of ImageNet known as miniImageNet and a ResNet152 model trained on the ILSVRC 2012 subset of ImageNet. Commonly used machine learning algorithms including logistic regression, support vector machines, random forests, nearest neighbour classification, naïve Bayes, and linear and quadratic discriminant analysis were evaluated on the extracted feature vectors. We also evaluated classification accuracy when subjecting the feature vectors to normalisation using p-norms. Algorithms originally developed for the classification of gene expression data—the nearest shrunken centroid algorithm and LDA ensembles obtained with random projections—were also included in the experiments, in addition to a cosine similarity classifier that has recently proved popular in few-shot learning. The results enable us to identify algorithms, normalisation methods and pre-trained feature extractors that perform well in cross-domain few-shot learning. We show that the cosine similarity classifier and   ℓ² -regularised 1-vs-rest logistic regression are generally the best-performing algorithms. We also show that algorithms such as LDA yield consistently higher accuracy when applied to   ℓ² -normalised feature vectors. In addition, all classifiers generally perform better when extracting feature vectors using the ResNet152 model instead of the ResNet10 model.

year: 2020

title: Weaving Mātauranga Māori into climate change education with primary school students in New Zealand

authors: Jackson, Ryan

abstract: This thesis explores the integration of Indigenous knowledge, specifically mātauranga Māori, into climate change education for primary school children in Aotearoa (New Zealand). It engages with pūrākau (verbal narratives that encode knowledge) gifted by the local hapū (Rangitāne sub-tribe) to a kura (school). The study contributes to the growing body of research that addresses a gap in research and teaching practice, which, despite global calls for action, remains inconsistent in teaching at this age level or lacks direct curriculum guidance in Aotearoa.

The main question for this study was: In what ways can Rangitāne pūrākau engage and further the understanding of Year five and six ākonga (learners) in learning about climate change within a kura in the lower North Island of Aotearoa?

This research used a mix of different research methods to explore this question. A learning intervention wove a pūrākau, climate change concepts and kai (food) together to create a ten-week learning programme. This learning intervention was then implemented by a kaiako (teacher) with a group of 21 Year five and six ākonga. Data were collected using pre- and post-tests to assess what students learned during the learning intervention, a group interview with the ākonga to hear their voices in the study, and an interview with their kaiako to gain insights into how engaged the ākonga were, their opinion on their understandings, and how aspects of the intervention went.

Analysis of the findings shows that the use of pūrākau and other forms of mātauranga Māori helped ākonga connect deeply with the material, which contributed positively to their engagement and to them gaining a sound understanding of complex climate change concepts. The research suggests that presenting scientific information through a familiar cultural lens contributed to making the topic less confusing and easier for the ākonga to grasp. This approach not only helped to improve their understanding but also enhanced the mana of ākonga Māori and their connection to the whenua (the land).

This research has several main implications. The findings provide evidence that incorporating Indigenous knowledge has wider educational benefits in climate change education, supporting the body of research that states what works well for ākonga Māori works well for all learners. The research also highlighted the importance of the concept of ako, literally meaning ‘to teach and learn,’ as the kaiako showed strength in being able to learn alongside ākonga. The thesis supports the use of Indigenous knowledge systems such as pūrākau alongside Western knowledge systems to develop holistic learning experiences. A recommendation from this thesis is to further explore the development of a climate change education model, relevant to the context of Aotearoa, which would centralise Indigenous knowledge. This could be modelled in the form of a waka hourua (double hulled ocean voyaging canoe) with the tohunga (knowledge holder and expert) on board signifying the use of Indigenous knowledge systems, with narratives such as pūrākau, playing a part in climate change education.

text: Weaving Mātauranga Māori into climate change education with primary school students in New Zealand This thesis explores the integration of Indigenous knowledge, specifically mātauranga Māori, into climate change education for primary school children in Aotearoa (New Zealand). It engages with pūrākau (verbal narratives that encode knowledge) gifted by the local hapū (Rangitāne sub-tribe) to a kura (school). The study contributes to the growing body of research that addresses a gap in research and teaching practice, which, despite global calls for action, remains inconsistent in teaching at this age level or lacks direct curriculum guidance in Aotearoa.

The main question for this study was: In what ways can Rangitāne pūrākau engage and further the understanding of Year five and six ākonga (learners) in learning about climate change within a kura in the lower North Island of Aotearoa?

This research used a mix of different research methods to explore this question. A learning intervention wove a pūrākau, climate change concepts and kai (food) together to create a ten-week learning programme. This learning intervention was then implemented by a kaiako (teacher) with a group of 21 Year five and six ākonga. Data were collected using pre- and post-tests to assess what students learned during the learning intervention, a group interview with the ākonga to hear their voices in the study, and an interview with their kaiako to gain insights into how engaged the ākonga were, their opinion on their understandings, and how aspects of the intervention went.

Analysis of the findings shows that the use of pūrākau and other forms of mātauranga Māori helped ākonga connect deeply with the material, which contributed positively to their engagement and to them gaining a sound understanding of complex climate change concepts. The research suggests that presenting scientific information through a familiar cultural lens contributed to making the topic less confusing and easier for the ākonga to grasp. This approach not only helped to improve their understanding but also enhanced the mana of ākonga Māori and their connection to the whenua (the land).

This research has several main implications. The findings provide evidence that incorporating Indigenous knowledge has wider educational benefits in climate change education, supporting the body of research that states what works well for ākonga Māori works well for all learners. The research also highlighted the importance of the concept of ako, literally meaning ‘to teach and learn,’ as the kaiako showed strength in being able to learn alongside ākonga. The thesis supports the use of Indigenous knowledge systems such as pūrākau alongside Western knowledge systems to develop holistic learning experiences. A recommendation from this thesis is to further explore the development of a climate change education model, relevant to the context of Aotearoa, which would centralise Indigenous knowledge. This could be modelled in the form of a waka hourua (double hulled ocean voyaging canoe) with the tohunga (knowledge holder and expert) on board signifying the use of Indigenous knowledge systems, with narratives such as pūrākau, playing a part in climate change education.

year: 2025