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Ornamenting the body: [Jewellery and Titirangi modernism through the lens of gender]
No full textMy creative practice has evolved through my PhD research, exploring the languages and practices of International Style modernist architecture and jewellery. Challenging current notions of architectural image-making, I’ve created innovative artefacts that adorn the female form, with the understanding that this ornamentation directs our focus, creates experiences, enhances aesthetics, and transforms the body into an exhibition site. These ornaments embody symbolic roles that transcend time, place, and purpose and are experienced individually and socially
Anticipatory Thinking and Sensemaking for Educational Design in an Era of Disruption: General Morphological Analysis Applied to Global Service Learning
No full textThis thesis presents an autoethnographic exploration of anticipatory thinking and sensemaking in educational design amid disruption, with a focus on improving Global Service Learning (GSL) for engineering students. Originally centered on enhancing GSL experiences, the research was significantly redirected by the COVID-19 pandemic, offering valuable insights into designing education for uncertain and rapidly changing environments. In navigating today’s volatile, uncertain, complex, and ambiguous (VUCA) world, educators face ‘wicked problems’—deeply interconnected, value-laden challenges that resist straightforward solutions. Addressing these requires a shift in approach. Anticipatory design thinking, sensemaking, and futures studies offer powerful tools for engaging with such complexity. Anticipatory design involves envisioning future scenarios to guide present decisions; sensemaking enables individuals to interpret dynamic environments; and futures studies support both through forecasting and scenario planning. GSL, traditionally a collaborative effort between students and communities (e.g., engineering students designing infrastructure), faces new challenges in VUCA conditions. These include integrating GSL into curricula, preparing students—especially for remote engagement—and ensuring meaningful, equitable learning experiences. Critical considerations include project duration, suitability, and avoiding colonial dynamics in partnerships. Recognising education as a complex adaptive system, this research draws on systems thinking to respond to D-VUCA conditions (disruption, volatility, uncertainty, complexity, ambiguity). Systems thinking, with its emphasis on interconnections and feedback loops, supports resilience and adaptive change in educational contexts. Conventional planning models often fall short in addressing GSL’s complexity under VUCA conditions. While suspending GSL until stability returns is one option, it is increasingly impractical. Instead, this thesis advocates for developing flexible, responsive GSL models tailored to VUCA realities—models that reflect both student and community needs. To support decision-making in this context, the study applies General Morphological Analysis (GMA), a non-quantitative method for structuring and analysing complex problems across technological, organisational, and social dimensions. GMA facilitates sensemaking by uncovering assumptions and unknowns, which are often more critical than the models themselves. The research involved participatory development of four scenarios, two multidimensional GSL frameworks, and associated response descriptors. Initial recommendations emerged from this process, followed by a critical assessment that highlighted areas for further inquiry, such as the interplay between dependency and learning. To validate these insights, 31 interviews were conducted with GSL practitioners. Thematic analysis confirmed the relevance of the GMA model and informed final recommendations for GSL in a VUCA world. Ultimately, this thesis underscores the value of reflective practice and iterative research in educational design. By integrating anticipatory thinking, sensemaking, and futures studies, it offers a robust framework for addressing wicked problems and fostering resilience in global education
Improving fairness in AI systems: A framework for bias mitigation
No full textRESEARCH QUESTIONS
• RQ1: How efficient are fairness metrics in effectively detecting and measuring bias across single and intersectional demographic groups?
• RQ2: How do bias mitigation techniques affect the balance between fairness and accuracy when applied at different stages of the AI development lifecycle?
• RQ3: To what extent do data-level augmentation strategies, such as SMOTE variants and GAN, affect group fairness outcomes and predictive performance?
• RQ4: How different are patterns of bias in Aotearoa New Zealand from global benchmark datasets?
• RQ5: How can a structured fairness evaluation framework be adapted to the Aotearoa New Zealand context?
ABSTRACT
Artificial Intelligence (AI) technologies are extensively adopted in essential sectors, such as healthcare, finance, and employment. Despite their effectiveness and predictive capabilities, AI systems remain vulnerable to bias, particularly when trained on data embedded with historical inequalities. These biases often reproduce existing systemic disparities through automated decisions, disproportionately affecting responsibilities populations. In Aotearoa New Zealand, ensuring fairness in AI carries additional importance due to the cultural and legal responsibilities mandated under the fragmented Tiriti o Waitangi, which advocates for equity for the Indigenous Māori population. Despite the introduction of a range of global and national-level initiatives, such as regulatory policies, data interventions, and model-based fairness techniques, their practical implementation remains fragmented and inconsistently effective in practice.
This thesis proposes a reproducible, multi-phase fairness framework for bias mitigation across the machine learning (ML) development lifecycle. It integrates and evaluates the effectiveness of multiple bias mitigation strategies across three critical phases of the model development: Reweighing at data pre-processing, Adversarial Debiasing (ADB) with in-processing model training, and Calibrated Equalised Odds (CEO) for post-processing of predictions. The framework is eval uated using group fairness metrics, including Statistical Parity Difference (SPD) and Disparate Impact (DI), Equal Opportunity Difference (EOD), and Average Odds Difference (AOD), leveraging IBM’s AI Fairness 360 (AIF360) toolkit.
These are assessed in conjunction with standard performance metrics such as Accuracy and Balanced Accuracy (BA) across four ML models: Random For est (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Ma chine (LightGBM), and Tabular Neural Network (TabNet) using a combination of global and Aotearoa New Zealand based datasets. Global datasets include the United States (U.S) Adult Census Income, U.S Diabetes, and Taiwan Credit datasets, while local data from Aotearoa New Zealand comprises the 2023 Cen sus and Accident Compensation Corporation (ACC) Claims, accessed via the Integrated Data Infrastructure (IDI) DataLab with appropriate approval from Statistics New Zealand (Stats NZ).
To assess the performance and enable comparison, we investigated widely adopted data balancing methods and our suggested multi-stage fairness framework. Our experimental results reveal that widely Adversarial data balancing methods, including Synthetic Minority Oversampling Technique (SMOTE) and Generative Adversarial Networks (GAN) augmentation, were ineffective in improving fairness. In many cases, applying these strategies further intensified existing disparities by skewing demographic representation, especially for the underrepresented groups. In contrast, the proposed framework consistently showed improvement of fairness under both attribute-specific and intersectional configurations. For example, the Adult dataset showed a 99.82% improvement in DI and a 91.67% reduction in EOD when evaluated using LightGBM. In the Aotearoa New Zealand Census dataset, RF achieved a 72.55% improvement in DI and an 89.45% boost in EOD. Intersectional fairness mitigation also shows significant improvement across all fairness metrics, validating the framework’s efficiency in intricate, real-world contexts. Significantly, these enhancements in fairness were achieved without compromising model performance. All models showed increasing Accuracy and BA, with most exceeding a 15% gain. The findings validate the practical efficacy of the proposed multi-stage framework in enhancing fairness in AI systems across varied and equity-sensitive environments. The study offers a scalable and culturally informed methodology for AI fairness, particularly pertinent to equity-sensitive applications in Aotearoa New Zealand and similar global contexts.
[NOTE: Māori Advisor: Diane Tamati
Puna Kōrero: Decolonial design and indigenous placemaking in the Wairaka Precinct
No full textINTRODUCTION
The current and former campus areas of Te Whare Wānanga o Wairaka (Unitec Institute of Technology) are undergoing massive change because of the Carrington Residential Development, an urban intensification project that will eventually account for tens of thousands of new residents and visitors in this area. Puna (pools, streams) nurture life in and around their flow paths, but they are also vulnerable to the negative consequences of the prevailing urban development paradigm. Many puna traverse our campus, connecting it with other parts of the Albert Eden local board area and beyond. Puna Kōrero is an interdisciplinary design research project providing opportunities for ontological reorientations in the Wairaka precinct of Tāmaki Makaurau (Auckland City), such shifts in worldviews hold the potential to improve the quality of relationships between people and this place over time, particularly its wāhi tapu (sacred sites). Te Wai Unuroa o Wairaka (an aquifer-fed freshwater stream) and Te Rangimarie Pā Harakeke (a plantation of flax).
Developed over several months, the project brought together staff, students and the local community across three collaborative strands culminating in a public event celebrating Matariki (the Māori new year) through a working bee to care for the whenua (land), a projection-mapped motion design show, the launching of a new puoro ataata (music video) entitled Manaakitia, and the prototyping of a mobile app to share stories of significant sites of the campus inclusive of the marae (Māori meeting house) and its wāhi tapu.
[Tanya White now known as Hinewaimarama Reihana-White
Optimized hybrid deep learning for advanced persistent threat detection
No full textAdvanced Persistent Threats (APTs) pose a substantial threat to contemporary cybersecurity systems due to their covert behavior, ability to embed themselves, and remain active undetected. Although deep learning structures, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for intrusion detection have been promising, there are few works on hybrid frameworks with optimal feature selection.
In this study, we compare the effectiveness of the four hybrid deep learning architectures: CNN, LSTM, BiLSTM, and CNN-BiLSTM with attention. To tune these models, we used Particle Swarm Optimization (PSO) for feature selection, to reduce dimensionality of features, optimize accuracy, and computational time of prediction. To address the class imbalance problem in multiclass classification, the Synthetic Minority Over-sampling Technique (SMOTE) is combined with PSO.The models are evaluated on four established cybersecurity datasets, namely, Linux APT 2024, UNSW-NB15, CIC-IDS (2017–2019), and TON-IoT. Results show that the CNN-BiLSTM-attention model trained by PSO and SMOTE can reach at most statistical significance of 97% and F1-score = 0.98. By using this scheme, we achieve a gain of 3–7% accuracy on the deNoised model with 30% reduced training time. The model also has strong generalization to all APT categories.
In this paper, we present a scalable PSO-SMOTE-based detection framework for high-dimensional imbalanced security datasets. To the best of the author’s knowledge, it is also the first effort to perform an extensive comparison of hybrid deep learning models about the PSO-based feature selection. The results demonstrate that swarm intelligence and deep learning can be combined to form an effective & adaptive solution to real-time APT threat detection as well
A study of loneliness among senior Indian migrant women in New Zealand: Identifying the gaps, challenges, and opportunities for support
No full textRESEARCH QUESTIONS
The fundamental question of this research is as follows:
What are the lived experiences of older Indian migrant women in NZ? Through narrative analysis, this project seeks to understand elderly female Indian migrants anticipated advantages and disadvantages (including potential risks of loneliness) associated with ageing in New Zealand.
The following questions provide more specific exploration into key aspects of their experience. Q1. What insights have elderly Indian migrant women in New Zealand gained regarding loneliness and ageing in India through their observations of and interactions with their parents, grandparents, and other elderly relatives? How do these experiences inform their perceptions of old age?
Q2. What are the expectations and aspirations of older Indian migrant women regarding their ageing process in New Zealand, and how do they anticipate managing the risk of loneliness in their later years?
Q3. What evidence-based solutions, interventions, and support services can be developed or adapted to effectively mitigate loneliness among elderly Indian migrant women in New Zealand, and what role can cultural sensitivity and community engagement play in addressing this issue?
ABSTRACT
This study presents the lived experience of loneliness as experienced by elderly Indian migrant women living in New Zealand, whose social, emotional, and cultural experiences impact their ageing process. As the number of the elderly Indian migrant population expands and the growth of using aged-care services builds, this research examines the role of migration, cultural displacement, language gaps, and intergenerational shifts as key factors contributing to loneliness. The study is based on a narrative inquiry framework and social practice perspectives and draws on the personal accounts of Indian women aged over 65 years who live in different situations, such as rest homes, retirement villages or family dwellings within the community. Data were collected through semantically guided interviews and analysed thematically to gain insight into participants perceptions, desires, and coping strategies.
Findings reveal that participants experience isolation due to physical separation from family and cultural incongruities within the care paradigm in New Zealand. In contrast to the traditional Indian collective family system, which allows intergenerational support and companionship on a daily basis, these Indian women find themselves isolated in a culture that is highly individualistic. They feel even more lonely due to language barriers, absence of culturally personalised services and challenges in adapting to mainstream aged-care environments. Nevertheless, these research participants exhibit resilience through engagement in a range of religious activities and participatory community sessions, including the preservation of culture through yoga, prayer and storytelling. They also maintain connections to their family and community in India through digital technology.
This research highlights the urgent need for culturally informed interventions, such as faith based outreach, culturally specific elder care programmes, and digital literacy interventions aimed at enhancing the well-being of elderly Indian migrants. Partnerships with Indian cultural organisations such as Shanti Niwas and Bhartiya Samaj are identified as essential for fostering social inclusion, improving life satisfaction, and mitigating the health risks associated with long-term loneliness. By addressing gaps in understanding ethnic and gender-specific experiences of migrant ageing, this research advocates for inclusive eldercare policies grounded in cultural competence and equity. The findings offer practical implications for service providers, policymakers and community leaders in the co-designing of effective structures as the means of that honour the dignity, identity, and well-being of elderly Indian women migrants in New Zealand
Challenges to achieving sustainability in social housing construction in New Zealand
No full textThe housing crisis resulting in a lack of affordable housing has become an urgent problem that New Zealand faces due to various socio-economic and historical-political reasons. Social housing is one of the ways to address this problem and currently the country is experiencing a significant increase in the need for affordable and quality social housing. Given the growing need for social housing, it is vital to understand what actions need to be taken and by whom to balance the various aspects of sustainability of the social housing sector. This is more important as housing providers face various interrelated challenges, including financial, political, social, and institutional factors that significantly influence their activities. This paper aimed to identify the factors influencing the achievement of sustainability and, where possible, make recommendations to the management of housing providers and the government on how to overcome the emerging challenges. The results of this study reflected the practical experience of housing providers and showed that achieving true sustainability in this sector is possible only with a radical transformation of the existing relations. The recommendations developed in this paper propose that the state take on the function of a social customer, determining the need, parameters, and characteristics of social housing, while housing providers are proposed to become exclusively project operators, ensuring their economic efficiency. This study should become an occasion to think about the role and importance of social housing for society and encourage the government to change the established practice of manipulating housing policy to please political ambitions, putting social and environmental aspects at the forefront, i.e., to do what any government must do
The fake beast: A post-anthropocentric representation of wildlife in picture book illustration
No full textThis creative project explores illustration practices in children's storytelling, focusing on the visual representation of wildlife through a post-anthropocentric lens. My aim is to foster empathy and connections with nature by portraying animals authentically, free from anthropomorphic attributes in storybook form. I believe the way the animal kingdom has historically been introduced to young readers in many children's stories has reinforced a hierachical human perspective toward wildlife. Through the creation of a wordless illustrated storybook, I aim to present the natural world in a way inspired by post-anthropocentric theorists and their notions of co-existence and reciprocity in the human-nature relationship.
Told entirely through illustrations, there is no text included in the picture book. The images are the sole form of communication. The absence of text was a critical decision to convey the narrative through tone, rhythm and feeling. This approach gives readers the space to interpret and reconstruct the story in their own way, engaging with the images to fill in the gaps and reimagine their own narrative.
The story I am illustrating was written by Vladimir Arseniev, a renowned Russian geographer and naturalist whose passion was to study and explore the diversity of life and the land around them. It recounts an expedition in the forest of my home region, Primirskiy kray, where, at the time the story was written, indigenous world views and the influence of the Industrial Revolution were colliding.
The methodology is a practice-led exploration of visual storytelling strategies, aiming to address the following questions:
how might illustration portray the world of wildlife in the context of a post-anthropocentric view point, how do I develop an engaging illustrative language for a young audience in this context, and how might a story be told through images only. The creative practice-led research journey explores illustration strategies and techniques through experimentation with content and media
Exploring the current status of digital technology adoption in the New Zealand architecture-engineering-construction-operation (AECO) industry
No full textThe Architectural-Engineering- Construction-Operation (AECO) industries play a vital role in the progress and development of societies by collectively contributing to the national GDP, improving quality of life through housing and infrastructure, and generating employment opportunities. However, the AECO industry faces several challenges, including project delays, cost overruns, sustainability issues, environmental concerns, labour shortages, supply chain disruptions, regulatory changes, and difficulties in adopting technological advancements. These challenges are being met with various strategies by the industry, workforce training, Lean construction practices, sustainable measures, and technological innovation including Building Information Modeling (BIM) and other digital technologies.
In New Zealand, the construction industry also encounters similar issues and is working to overcome these challenges by adopting new technologies and best practices. This research aims to investigate the current status of digital technology adoption within the New Zealand construction sector. By utilizing a qualitative research methodology with a focus on document analysis, this study aimed to identify key digital technologies employed in New Zealand construction industries.
The findings will provide a comprehensive overview of the current state of digital technology utilization in New Zealand’s construction industry and offer insights for future research. Additionally, the results will assist industry practitioners in enhancing the integration of digital technologies to improve productivity and address industry challenges
In this paper...
No full textThis paper is a collection of responses from computer scientists to the silencing of science. Sustainability-driven computing research-encompassing equity, diversity, climate change, and social justice-is increasingly dismissed as 'woke' or even dangerous in many sociopolitical contexts. As misinformation, ideological polarisation, deliberate ignorance and reactionary narratives gain ground, how can sustainability research in computing continue to exist and make an impact? This paper explores these tensions through Fictomorphosis, a creative story retelling method that reframes contested topics through different genres and perspectives. By engaging computing researchers in structured narrative transformations, we investigate how sustainability-oriented computing research is perceived, contested, and can adapt in a post-truth world