title: ‘Revolting murder at Te Aroha’ in 1881

authors: Hart, Philip

abstract: The brutal murder of Hamiona Haira, who had been mining with his two brothers and his father-in-law, shocked the new settlement. Being a member of Ngati Koi and Ngati Hako, these hapu threatened utu on the Pakeha responsible, causing both Maori and Pakeha miners to abandon their claims at Tui. Suspicion immediately fell on John Procoffy, a Finn, and evidence was quickly collected, officials being anxious to obtain a speedy conviction to avoid an innocent Pakeha being killed in revenge. Rangatira agreed to let the courts deal with the case, although some Pakeha criticized the government for being too anxious to appease Maori.
After a coroner’s inquest, which included Maori as members of the jury, returned a verdict of murder by ‘person or persons unknown’, Procoffy faced two trials. As the prosecution was handicapped by the limitations of contemporary forensic skills, its case was largely circumstantial. Although the police were certain they had their murderer, others were not convinced, and there was a reluctance to convict because of the death penalty. The final outcome was a verdict of not guilty, which, as was pointed out, should have been ‘not proven’. 
Procoffy fled the country. His acquittal was accepted by Maori leaders, and calm returned to Te Aroha.

text: ‘Revolting murder at Te Aroha’ in 1881 The brutal murder of Hamiona Haira, who had been mining with his two brothers and his father-in-law, shocked the new settlement. Being a member of Ngati Koi and Ngati Hako, these hapu threatened utu on the Pakeha responsible, causing both Maori and Pakeha miners to abandon their claims at Tui. Suspicion immediately fell on John Procoffy, a Finn, and evidence was quickly collected, officials being anxious to obtain a speedy conviction to avoid an innocent Pakeha being killed in revenge. Rangatira agreed to let the courts deal with the case, although some Pakeha criticized the government for being too anxious to appease Maori.
After a coroner’s inquest, which included Maori as members of the jury, returned a verdict of murder by ‘person or persons unknown’, Procoffy faced two trials. As the prosecution was handicapped by the limitations of contemporary forensic skills, its case was largely circumstantial. Although the police were certain they had their murderer, others were not convinced, and there was a reluctance to convict because of the death penalty. The final outcome was a verdict of not guilty, which, as was pointed out, should have been ‘not proven’. 
Procoffy fled the country. His acquittal was accepted by Maori leaders, and calm returned to Te Aroha.

year: 2016

title: The Price of Mauri: Exploring the validity of Welfare Economics when seeking to measure Mātauranga Māori

authors: Awatere, Shaun

subjects: welfare economics

abstract: Since the 1980s New Zealand has pursued neo-classical or market-based policies with a particular fervour. Market-based options are seen by resource management decision makers as essential frameworks for efficiently allocating resources, an approach that continues to support the view of the inherent dominance of Western knowledge. This is particularly concerning, given that Māori (the indigenous people of New Zealand), have an important role to play in New Zealand resource management and perceive their own knowledge systems have been marginalised. The primary goal of this thesis is to explore the validity of welfare economics when seeking to measure quantitatively Mātauranga Māori or Māori views of the environment through the contingent valuation method. A contingent valuation study is carried out using three separate samples drawn from the general Māori population in Auckland city, a hāpu/sub-tribe indigenous to the Auckland isthmus, and drivers of motor vehicles in Auckland city. Data collection modes include a postal survey and face-to-face interviews. This thesis challenges the validity of political-legal ethnicity constructs to measure Mātauranga Māori. The search for a central tendency will lead to biased, misleading and inaccurate results. The thesis also challenges the validity of contingent valuation to produce true economic measures and to measure and identify Mātauranga Māori. Despite advances in analytical techniques, economic efficiency measures are always deficient, given the difficulty of capturing and anticipating all impacts and valuing them appropriately. Mātauranga Māori is derived from a Māori epistemology and should be considered or analysed with primary reference to this body of knowledge. Economic analysis is only one important cog in the machinery of resource management policy. Given that an economist's contribution to local and regional resource management is most valuable when focusing on the  economic efficiency  of the proposed resource allocation, it is appropriate that other perspectives such as Mātauranga Māori be considered.

text: The Price of Mauri: Exploring the validity of Welfare Economics when seeking to measure Mātauranga Māori Since the 1980s New Zealand has pursued neo-classical or market-based policies with a particular fervour. Market-based options are seen by resource management decision makers as essential frameworks for efficiently allocating resources, an approach that continues to support the view of the inherent dominance of Western knowledge. This is particularly concerning, given that Māori (the indigenous people of New Zealand), have an important role to play in New Zealand resource management and perceive their own knowledge systems have been marginalised. The primary goal of this thesis is to explore the validity of welfare economics when seeking to measure quantitatively Mātauranga Māori or Māori views of the environment through the contingent valuation method. A contingent valuation study is carried out using three separate samples drawn from the general Māori population in Auckland city, a hāpu/sub-tribe indigenous to the Auckland isthmus, and drivers of motor vehicles in Auckland city. Data collection modes include a postal survey and face-to-face interviews. This thesis challenges the validity of political-legal ethnicity constructs to measure Mātauranga Māori. The search for a central tendency will lead to biased, misleading and inaccurate results. The thesis also challenges the validity of contingent valuation to produce true economic measures and to measure and identify Mātauranga Māori. Despite advances in analytical techniques, economic efficiency measures are always deficient, given the difficulty of capturing and anticipating all impacts and valuing them appropriately. Mātauranga Māori is derived from a Māori epistemology and should be considered or analysed with primary reference to this body of knowledge. Economic analysis is only one important cog in the machinery of resource management policy. Given that an economist's contribution to local and regional resource management is most valuable when focusing on the  economic efficiency  of the proposed resource allocation, it is appropriate that other perspectives such as Mātauranga Māori be considered.

year: 2008

title: Mini-batching with fused training and testing for data streams processing on the Edge

authors: Luna, R

subjects: computer science

abstract: Edge Computing (EC) has emerged as a solution to reduce energy demand and greenhouse gas emissions from digital technologies. EC supports low latency, mobility, and location awareness for delay-sensitive applications by bridging the gap between cloud computing services and end-users. Machine learning (ML) methods have been applied in EC for data classification and information processing. Ensemble learners have often proven to yield high predictive performance on data stream classification problems. Mini-batching is a technique proposed for improving cache reuse in multi-core architectures of bagging ensembles for the classification of online data streams, which benefits application speedup and reduces energy consumption. However, the original mini-batching presents limited benefits in terms of cache reuse and it hinders the accuracy of the ensembles (i.e., their capacity to detect behavior changes in data streams). In this paper, we improve mini-batching by fusing continuous training and test loops for the classification of data streams. We evaluated the new strategy by comparing its performance and energy efficiency with the original mini-batching for data stream classification using six ensemble algorithms and four benchmark datasets. We also compare mini-batching strategies with two hardware-based strategies supported by commodity multi-core processors commonly used in EC. Results show that mini-batching strategies can significantly reduce energy consumption in 95% of the experiments. Mini-batching improved energy efficiency by 96% on average and 169% in the best case. Likewise, our new mini-batching strategy improved energy efficiency by 136% on average and 456% in the best case. These strategies also support better control of the balance between performance, energy efficiency, and accuracy.

text: Mini-batching with fused training and testing for data streams processing on the Edge Edge Computing (EC) has emerged as a solution to reduce energy demand and greenhouse gas emissions from digital technologies. EC supports low latency, mobility, and location awareness for delay-sensitive applications by bridging the gap between cloud computing services and end-users. Machine learning (ML) methods have been applied in EC for data classification and information processing. Ensemble learners have often proven to yield high predictive performance on data stream classification problems. Mini-batching is a technique proposed for improving cache reuse in multi-core architectures of bagging ensembles for the classification of online data streams, which benefits application speedup and reduces energy consumption. However, the original mini-batching presents limited benefits in terms of cache reuse and it hinders the accuracy of the ensembles (i.e., their capacity to detect behavior changes in data streams). In this paper, we improve mini-batching by fusing continuous training and test loops for the classification of data streams. We evaluated the new strategy by comparing its performance and energy efficiency with the original mini-batching for data stream classification using six ensemble algorithms and four benchmark datasets. We also compare mini-batching strategies with two hardware-based strategies supported by commodity multi-core processors commonly used in EC. Results show that mini-batching strategies can significantly reduce energy consumption in 95% of the experiments. Mini-batching improved energy efficiency by 96% on average and 169% in the best case. Likewise, our new mini-batching strategy improved energy efficiency by 136% on average and 456% in the best case. These strategies also support better control of the balance between performance, energy efficiency, and accuracy.

year: 2024