title: The Treaty of Waitangi and Research Ethics in Aotearoa

authors: Hudson, Maui

subjects: Treaty of Waitangi

abstract: Researchers, when engaging with Māori communities, are in a process of relationship building and this process can be guided by the principles of the Treaty of Waitangi, partnership, participation and protection. The main concerns for many indigenous peoples in research revolve around respect for their indigenous rights, control over research processes and reciprocity within research relationships to ensure that equitable benefits are realised within indigenous groups. Māori have identified similar issues and these concerns can be aligned with the principles of the Treaty of Waitangi. The relevance of the Treaty of Waitangi to research ethics is discussed and this paper suggests a revised interpretation of the treaty principles to incorporate the range of ethical issues that Māori have expressed as important.

text: The Treaty of Waitangi and Research Ethics in Aotearoa Researchers, when engaging with Māori communities, are in a process of relationship building and this process can be guided by the principles of the Treaty of Waitangi, partnership, participation and protection. The main concerns for many indigenous peoples in research revolve around respect for their indigenous rights, control over research processes and reciprocity within research relationships to ensure that equitable benefits are realised within indigenous groups. Māori have identified similar issues and these concerns can be aligned with the principles of the Treaty of Waitangi. The relevance of the Treaty of Waitangi to research ethics is discussed and this paper suggests a revised interpretation of the treaty principles to incorporate the range of ethical issues that Māori have expressed as important.

year: 2009

title: Improving prediction of potential for treatment completion at Tai Aroha

authors: Astridge Clarke,  Abigail Darcy

subjects: Treatment

abstract: Participant non-completion can be a major problem for treatment programmes. Staff may try to minimise non-completion by deciding who to select for treatment using professional intuition and referral information. It can be difficult for treatment staff to make these decisions regarding the potential for programme completion due to a lack of clear guidelines about what information to consider. This lack of clear guidelines can increase susceptibility to biases, and lead to unreliable and potentially inaccurate decisions. Research has identified ‘simple rules’ that are tools to aid decision-making quickly, effectively, and consistently. Therefore, using past treatment data, we1 aimed to identify predictors of programme completion vs. non-completion at Tai Aroha—a residential treatment programme based in Hamilton—to explore whether a “simple rule” can be developed from information readily available at the time of referral, to support staff decision-making when selecting offenders to attend treatment, with the potential to increase programme completion. We were successful in the development of a simple rule to aid Tai Aroha staff selection decisions. The implications, and limitations of our rule and our study are discussed, and future research relating to simple rules are identified.

text: Improving prediction of potential for treatment completion at Tai Aroha Participant non-completion can be a major problem for treatment programmes. Staff may try to minimise non-completion by deciding who to select for treatment using professional intuition and referral information. It can be difficult for treatment staff to make these decisions regarding the potential for programme completion due to a lack of clear guidelines about what information to consider. This lack of clear guidelines can increase susceptibility to biases, and lead to unreliable and potentially inaccurate decisions. Research has identified ‘simple rules’ that are tools to aid decision-making quickly, effectively, and consistently. Therefore, using past treatment data, we1 aimed to identify predictors of programme completion vs. non-completion at Tai Aroha—a residential treatment programme based in Hamilton—to explore whether a “simple rule” can be developed from information readily available at the time of referral, to support staff decision-making when selecting offenders to attend treatment, with the potential to increase programme completion. We were successful in the development of a simple rule to aid Tai Aroha staff selection decisions. The implications, and limitations of our rule and our study are discussed, and future research relating to simple rules are identified.

year: 2023

title: A simple approach to ordinal classification

authors: Frank, Eibe

abstract: Machine learning methods for classification problems commonly assume that the class values are unordered. However, in many practical applications, the class values do exhibit a natural order—for example, when learning how to grade. The standard approach to ordinal classification converts the class value into a numeric quantity and applies a regression learner to the transformed data, translating the output back into a discrete class value in a post-processing step. A disadvantage of this method is that it can only be applied in conjunction with a regression scheme.

In this paper, we present a simple method that enables standard classification algorithms to make use of ordering information in class attributes. By applying it in conjunction with a decision tree learner, we show that it outperforms the naive approach, which treats the class values as an unordered set. Compared to special-purpose algorithms for ordinal classification, our method has the advantage that it can be applied without any modification to the underlying learning scheme.

text: A simple approach to ordinal classification Machine learning methods for classification problems commonly assume that the class values are unordered. However, in many practical applications, the class values do exhibit a natural order—for example, when learning how to grade. The standard approach to ordinal classification converts the class value into a numeric quantity and applies a regression learner to the transformed data, translating the output back into a discrete class value in a post-processing step. A disadvantage of this method is that it can only be applied in conjunction with a regression scheme.

In this paper, we present a simple method that enables standard classification algorithms to make use of ordering information in class attributes. By applying it in conjunction with a decision tree learner, we show that it outperforms the naive approach, which treats the class values as an unordered set. Compared to special-purpose algorithms for ordinal classification, our method has the advantage that it can be applied without any modification to the underlying learning scheme.

year: 2001