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Māori data governance model
(Te Kāhui Raraunga, 2023) Kukutai, Tahu; Campbell-Kamareira, Kyla; Mead, Aroha Te Pareake; Mikaere, Kirikowhai; Moses, Caleb; Whitehead, Jesse; Cormack, Donna
This report describes the Māori Data Governance Model that has been designed by Māori data experts for use across the Aotearoa New Zealand public service. Māori data is a taonga that requires culturally grounded models of protection and care. The Model provides guidance for the system-wide governance of Māori data, consistent with the Government’s responsibilities under te Tiriti o Waitangi. The Model is intended to assist all agencies to undertake Māori data governance in a way that is values-led, centred on Māori needs and priorities, and informed by research. This is important because existing government data processes and practices are failing to meet Māori informational needs. The Vision, Tuia te korowai o Hine-Raraunga – Data for self-determination, enables iwi, hapū and Māori organisations, businesses and communities to pursue their own goals for cultural, social, economic and environmental wellbeing. Eight Data Pou or pillars define critical areas of data governance and specify the actions that should be undertaken to realise six desired outcomes. The Model does not cover every element of data governance; instead, it focuses on key priorities and actions, against which agencies can assess their level of data maturity. The Model explicitly recognises the need for changes to system leadership, policies and legal settings so that Māori can exercise authority over Māori data to reduce unethical data use and strengthen outcomes for individuals, whānau and communities. The report also identifies the need for strategic investment in a Mana Motuhake data system that sits outside of the public sector to ensure iwi and hapū sovereignty over iwi and hapū data.
Removal of estrone (E1) in water: A comparative study of UV, natural light, and algal treatments
(The University of Waikato, 2025) Chen, Yixin; Lay, Mark C.
Estrone (E1) is an environmental estrogen that threatens both man's health and the ecosystem due to its endocrine-disrupting action. This research compared and evaluated three treatment methods - ultraviolet (UV) irradiation, natural sunlight exposure, and algal treatment with Spirogyra, based on the removal efficiency of E1 under controlled laboratory conditions. Because LC–MS identification of the first-round samples is still pending, a second round of experiments was conducted using UV–Vis spectrophotometry to track E1 degradation at higher concentrations (0–10 mg/L). The results demonstrated that natural sunlight achieved the highest E1 removal efficiency, with the 10 mg/L samples showing an 84.86% decline in absorbance by the second day. The UV group showed consistent but less degradation in E1 removal, while the algal group showed the least removal in E1. Further analyses, including light-field mapping, short-term reproducibility tests, pH condition experiments, and concentration-time curves, also provided mechanistic explanations and reinforced the robustness of these findings. This study gives proof that the non-catalytic photodegradation of E1 in natural provides experimental support for the development of energy-saving and green water treatment processes.
The estimation and interpretation of coefficients in panel gravity models of migration
(Springer Nature, 2019) Cameron, Michael Patrick; Poot, Jacques
In this paper, we demonstrate that the conventional ordinary least squares and fixed effects estimators of classical gravity models of migration are biased, and that the interpretation of coefficients in the fixed effects gravity model is typically incorrect. We then present a best linear unbiased (BLU) estimator for gravity models of migration, and illustrate its application with inter-regional data from New Zealand. The results demonstrate that the standard ordinary least squares and fixed effect models lead to biased coefficients on population. Alternative estimates that are BLU are provided for a data generating process with fixed origin and destination effects. The coefficients on population must be interpreted in this model as growth rate effects rather than level effects. Our findings also have significance for other types of spatial interaction modelling.
He Pikinga Waiora Kimi Ora lifestyle programme: Case study of a successful community-based indigenous diabetes intervention
(Pasifika Medical Association Group (PMAG), 2021) Masters-Awatere, Bridgette; Cassim, Shemana; Tamatea, Jade; Scott, Nina; Simpson, Chae; Paekau, Cherie
AIM: To co-design and implement a whānau-centred, community-based lifestyle programme (Kimi Ora) intended to ensure no worsening of HbA1c and to improve wellbeing for Māori whānau and communities with diabetes or pre-diabetes. METHODS: Māori healthcare providers, community members, research advisors and wider stakeholders used a co-design process underpinned by He Pikinga Waiora to collaboratively develop and implement Kimi Ora Control group comparisons and participants were recruited from Te Kōhao Health. Multi-method monitoring and collection captured individual, whānau and community data. RESULTS: Kimi Ora was run in two communities in Aotearoa New Zealand. In total, there were 35 participants who took part in an eight-week programme offered five times alongside a comparison group comprising 21 participants. Kimi Ora resulted in significant improvements on all biomedical measures compared to baseline, and participants had gains relative to the comparison group for variables including weight, BMI, blood pressure and waist measurement. Of particular note was the 100% retention rate and sustained community support for Kimi Ora. CONCLUSIONS: Outcomes from Kimi Ora demonstrate this programme, which was actively tailored for and worked with Māori communities in a responsive and flexible manner, resulted in successful biomedical outcomes, high engagement and high retention.
The alienation of the Opuatia block: A GIS case study report written for Counting our Tūpuna
(Te Ngira: Institute for Population Research, the University of Waikato, 2021) Whitehead, Jesse
This report provides an analysis and visualisation of the fragmentation and alienation of the Opuatia block (Opuatia hereafter), as well as changes in its ownership and geographic structure since 1866. The three main aims are to quantify the amount of Opuatia remaining in Ngāti Tiipa ownership at the end of each decade, identifying critical moments in time, and highlighting the processes through which Opuatia was alienated and fragmented. These questions are addressed through a bespoke method that was developed to link data from several sources. These include (but are not limited to) historic records of land alienation, held by the Māori Land Court, and geospatial information available through Land Information New Zealand. The key findings show that approximately 94% of the original Opuatia has been alienated, with only small parcles of land remaining in Ngāti Tiipa ownership. The late 1890s and early 1900s were a criticial period where over 80% of alienations were carried out. The results inidcate that the Crown was a key driver of this process, directly alienating more than 18,000 acres from Opuatia, much of which was taken on a single day in 1896. Individual settlers were responsible for most other alienations, and some of the same names appear several times in the historical record as settler families alienated clusters of land to build up significant stakes in the area. Finally, another result of this report is the method itself, which outlines an approach for tracing a defined block of land through the Native Land Court and linking this information to geospatial datasets. The limitations of this report include the poor interoperability of key data sources, and the barriers that this presents to other hapū or organisations who may want to replicate this work. A more detailed investigation of archival records is required to examine the specific reasons behind why each parent block, and the subdivisions with them, was alienated from Ngāti Tiipa ownership.