title: Preface: Learning and teaching across geographical curricula

authors: Chalmers, Lex

subjects: New Zealand

abstract: In January 2010, the Waikato Branch of the New Zealand Geographical Society hosted the Positioning Geography: Strategic Issues in Geographical Education Conference. The conference was held at the University of Waikato in Hamilton and offered plenary sessions, the presentation of themed papers, workshops, field trips and round table sessions.

text: Preface: Learning and teaching across geographical curricula In January 2010, the Waikato Branch of the New Zealand Geographical Society hosted the Positioning Geography: Strategic Issues in Geographical Education Conference. The conference was held at the University of Waikato in Hamilton and offered plenary sessions, the presentation of themed papers, workshops, field trips and round table sessions.

year: 2010

title: Getting back to work after the baby: A quantitative exploratory study

authors: Cochrane, Michael

subjects: Clinical psychology

abstract: The postnatal period is a time of increased stress and sometimes high levels of anxiety and depression for mothers. While there has been substantial research on postnatal mental health problems and interventions, very little is known about the transition to work after maternity leave, and how work interacts with perinatal emotional distress. This work investigates how the mental health of mothers who are returning to work relates to their demographic characteristics as well as the interpersonal and administrative features of their work. These data were gathered with a comprehensive questionnaire that measured demographics, levels of distress, work characteristics, presenteeism, illness disclosure, co-worker support, manager support, and job quality. A total of 246 working mothers from New Zealand with children under 24 months old were included in the sample. The sample showed significantly higher levels of distress on the Depression, Anxiety, and Stress Scale (DASS-21) than the normative population. Education level and financial situation were negatively correlated with DASS-21 scores, and the experience of previous perinatal distress (PND) and working from home were positively associated with DASS-21 scores. Presenteeism, attitudes to illness disclosure, and co-worker support all had medium strength significant correlations with DASS-21 scores, while manager support showed a weak significant correlation with the DASS-21. A t-test showed that mean DASS-21 scores of Māori mothers were significantly higher than those of Pākehā mothers. Co-worker support, financial situation, being Māori, and job location all contributed significantly to the variance of DASS-21 scores within the full sample. Presenteeism, attitude to illness disclosure, and co-worker support all contributed to the variance of DASS-21 scores for the sub-sample that reported experiencing PND. These findings provide evidence that work related factors should be a focus in further research into perinatal distress. Future research might also explore these factors in a larger, more diverse sample to allow for examination of the experiences of mothers from a variety of ethnic, cultural, vocational, and socioeconomic groups.

text: Getting back to work after the baby: A quantitative exploratory study The postnatal period is a time of increased stress and sometimes high levels of anxiety and depression for mothers. While there has been substantial research on postnatal mental health problems and interventions, very little is known about the transition to work after maternity leave, and how work interacts with perinatal emotional distress. This work investigates how the mental health of mothers who are returning to work relates to their demographic characteristics as well as the interpersonal and administrative features of their work. These data were gathered with a comprehensive questionnaire that measured demographics, levels of distress, work characteristics, presenteeism, illness disclosure, co-worker support, manager support, and job quality. A total of 246 working mothers from New Zealand with children under 24 months old were included in the sample. The sample showed significantly higher levels of distress on the Depression, Anxiety, and Stress Scale (DASS-21) than the normative population. Education level and financial situation were negatively correlated with DASS-21 scores, and the experience of previous perinatal distress (PND) and working from home were positively associated with DASS-21 scores. Presenteeism, attitudes to illness disclosure, and co-worker support all had medium strength significant correlations with DASS-21 scores, while manager support showed a weak significant correlation with the DASS-21. A t-test showed that mean DASS-21 scores of Māori mothers were significantly higher than those of Pākehā mothers. Co-worker support, financial situation, being Māori, and job location all contributed significantly to the variance of DASS-21 scores within the full sample. Presenteeism, attitude to illness disclosure, and co-worker support all contributed to the variance of DASS-21 scores for the sub-sample that reported experiencing PND. These findings provide evidence that work related factors should be a focus in further research into perinatal distress. Future research might also explore these factors in a larger, more diverse sample to allow for examination of the experiences of mothers from a variety of ethnic, cultural, vocational, and socioeconomic groups.

year: 2023

title: Visualising categorical data: Linguistic case studies from te Reo Māori and New Zealand English

authors: Trye, David

subjects: visualisation

abstract: Categorical variables are prevalent in real-world datasets across numerous domains, yet few visualisation techniques accommodate them effectively. This is especially true of datasets comprising three or more categorical variables, termed multivariate categorical data. Visualising such data is challenging due to the lack of inherent ordering of nominal categories, the so-called ‘curse of dimensionality’, and the potential variability in the number of categories per variable. Corpus linguistics, which involves the study of large digital collections of naturally occurring language, serves as the primary application domain in this thesis. This domain was chosen because it is rich in multivariate categorical data and, at the same time, is often visualised using only basic techniques.

This thesis contributes to the area of categorical data visualisation in several ways. First, we propose a taxonomy of techniques for visualising categorical data, highlight limitations of existing solutions, and identify relevant analysis tasks. Building on this foundation, the thesis introduces novel techniques and enhancements for visualising datasets involving multiple categorical variables. We focus on adapting the layout and interactive capabilities of an existing technique that uses a matrix of heatmaps to represent pairwise category intersections. These modifications show that directly visualising statistical test results for categorical data can be beneficial for exploring bivariate patterns and associations. Furthermore, we contribute the design, implementation and evaluation of a novel technique called MultiCat, which is not restricted to pairwise intersections but rather facilitates analysis of relationships among multiple variables simultaneously. Both these techniques are interactive and offer greater scalability than existing alternatives, thereby affording new possibilities for analysing multivariate categorical data. However, since categorical variables can occur within more complex data structures, we also consider their presence in networks and hypergraphs, which require specialised methods.

To demonstrate the application of these techniques, we draw on two linguistic case studies that focus on languages of special significance in Aotearoa New Zealand. Addressing the low-resource status of Māori, the country’s Indigenous language, we first contribute two related Twitter datasets—a monolingual Māori corpus and a mixed–language Māori–English corpus—together with an architecture for differentiating Māori and English words. Our initial case study uses the monolingual Māori corpus and proposed visualisation techniques to investigate grammatical possession in Māori, offering fresh insights into the linguistic practices of contemporary speakers. The second case study uses networks and hypergraphs with categorical attributes to explore Māori loanword co-occurrence in New Zealand English newspaper articles. We find that loanwords tend not to occur in isolation and that New Zealanders are still importing new (unlisted) borrowings from Māori.

Ultimately, the techniques developed in this thesis have broad applications both within and beyond the corpus linguistics community. By enabling more effective visualisation and analysis of multivariate categorical data, this research has the potential to facilitate deeper insights into domains as diverse as education, healthcare, business and science.

text: Visualising categorical data: Linguistic case studies from te Reo Māori and New Zealand English Categorical variables are prevalent in real-world datasets across numerous domains, yet few visualisation techniques accommodate them effectively. This is especially true of datasets comprising three or more categorical variables, termed multivariate categorical data. Visualising such data is challenging due to the lack of inherent ordering of nominal categories, the so-called ‘curse of dimensionality’, and the potential variability in the number of categories per variable. Corpus linguistics, which involves the study of large digital collections of naturally occurring language, serves as the primary application domain in this thesis. This domain was chosen because it is rich in multivariate categorical data and, at the same time, is often visualised using only basic techniques.

This thesis contributes to the area of categorical data visualisation in several ways. First, we propose a taxonomy of techniques for visualising categorical data, highlight limitations of existing solutions, and identify relevant analysis tasks. Building on this foundation, the thesis introduces novel techniques and enhancements for visualising datasets involving multiple categorical variables. We focus on adapting the layout and interactive capabilities of an existing technique that uses a matrix of heatmaps to represent pairwise category intersections. These modifications show that directly visualising statistical test results for categorical data can be beneficial for exploring bivariate patterns and associations. Furthermore, we contribute the design, implementation and evaluation of a novel technique called MultiCat, which is not restricted to pairwise intersections but rather facilitates analysis of relationships among multiple variables simultaneously. Both these techniques are interactive and offer greater scalability than existing alternatives, thereby affording new possibilities for analysing multivariate categorical data. However, since categorical variables can occur within more complex data structures, we also consider their presence in networks and hypergraphs, which require specialised methods.

To demonstrate the application of these techniques, we draw on two linguistic case studies that focus on languages of special significance in Aotearoa New Zealand. Addressing the low-resource status of Māori, the country’s Indigenous language, we first contribute two related Twitter datasets—a monolingual Māori corpus and a mixed–language Māori–English corpus—together with an architecture for differentiating Māori and English words. Our initial case study uses the monolingual Māori corpus and proposed visualisation techniques to investigate grammatical possession in Māori, offering fresh insights into the linguistic practices of contemporary speakers. The second case study uses networks and hypergraphs with categorical attributes to explore Māori loanword co-occurrence in New Zealand English newspaper articles. We find that loanwords tend not to occur in isolation and that New Zealanders are still importing new (unlisted) borrowings from Māori.

Ultimately, the techniques developed in this thesis have broad applications both within and beyond the corpus linguistics community. By enabling more effective visualisation and analysis of multivariate categorical data, this research has the potential to facilitate deeper insights into domains as diverse as education, healthcare, business and science.

year: 2024