title: Conceptualising leadership in early childhood education in Aotearoa New Zealand

authors: Thornton, Kate

subjects: education

abstract: The New Zealand Teachers Council Te Pouherenga Kaiako o Aotearoa is pleased to publish this occasional paper focused on leadership in early childhood education in Aotearoa New Zealand. The project that led to this publication grew out of a desire of the Council’s Early Childhood Education Advisory Group to promote some action on leadership development specifically for ECE. Whereas there is a well-developed leadership strategy for the school sector there is no equivalent support for teachers in ECE. Yet it is well established in the literature that an effective professional learning community is most likely to result from leadership that has learning as the central focus. Thus the absence of a cohesive leadership strategy was seen as a significant risk to professional initiatives supporting quality teaching in ECE.

text: Conceptualising leadership in early childhood education in Aotearoa New Zealand The New Zealand Teachers Council Te Pouherenga Kaiako o Aotearoa is pleased to publish this occasional paper focused on leadership in early childhood education in Aotearoa New Zealand. The project that led to this publication grew out of a desire of the Council’s Early Childhood Education Advisory Group to promote some action on leadership development specifically for ECE. Whereas there is a well-developed leadership strategy for the school sector there is no equivalent support for teachers in ECE. Yet it is well established in the literature that an effective professional learning community is most likely to result from leadership that has learning as the central focus. Thus the absence of a cohesive leadership strategy was seen as a significant risk to professional initiatives supporting quality teaching in ECE.

year: 2009

title: Prompt-GAN – Customisable hate speech and extremist datasets via radicalised neural language models

authors: Govers, Jarod

subjects: Artificial Intelligence

abstract: Online hate speech and violent extremism knows no borders, no political boundaries, no remorse. Researchers face an uphill battle to collect hate speech data in volumes and topical diversity suitable for training state-of-the art content-moderation systems. Neural language models ushered in a new era of synthetic data generation in use across various businesses, all despite calls for research to protect against unintended toxic output. We present a method for radicalising pre-trained neural language models to identify real online hate speech, as well as present the risks of rouge radicalised AI bots which could undermine our trust in social media. We present Prompt-GAN, a prompt-tuning adversarial approach with three achievements. We demonstrate prompt-tuning’s ability to generate realistic types of hate and non-hate speech which mimics political extremist discourse. Prompt-GAN’s architecture offers a twofold reduction in memory and runtime requirements compared to fine-tuning. Finally, Prompt-GAN improves hate speech classification F1-scores by up to 10.1% and sets a new record in neural language simulation compared to the current state-of-the-art across three benchmark datasets.

text: Prompt-GAN – Customisable hate speech and extremist datasets via radicalised neural language models Online hate speech and violent extremism knows no borders, no political boundaries, no remorse. Researchers face an uphill battle to collect hate speech data in volumes and topical diversity suitable for training state-of-the art content-moderation systems. Neural language models ushered in a new era of synthetic data generation in use across various businesses, all despite calls for research to protect against unintended toxic output. We present a method for radicalising pre-trained neural language models to identify real online hate speech, as well as present the risks of rouge radicalised AI bots which could undermine our trust in social media. We present Prompt-GAN, a prompt-tuning adversarial approach with three achievements. We demonstrate prompt-tuning’s ability to generate realistic types of hate and non-hate speech which mimics political extremist discourse. Prompt-GAN’s architecture offers a twofold reduction in memory and runtime requirements compared to fine-tuning. Finally, Prompt-GAN improves hate speech classification F1-scores by up to 10.1% and sets a new record in neural language simulation compared to the current state-of-the-art across three benchmark datasets.

year: 2022

title: Māori data sovereignty and privacy

authors: Kukutai, Tahu

abstract: Privacy is a fundamental human right. One of its most important aspects is information privacy – providing individuals with control over the way in which their personal data is collected, used, disclosed and otherwise handled. Existing information privacy regulation neither recognises nor protects the collective privacy rights of Indigenous peoples. This paper explores Indigenous data
privacy, and the challenges and opportunities, in the context of Aotearoa. It has two aims: to identify gaps in existing data privacy approaches with regards to Indigenous data, and to provide a foundation for progressing alternative privacy paradigms. We argue that while personal data protection is necessary, it is insufficient to meet the needs of Māori and Aotearoa more broadly. In so doing, we draw on three areas of research: Indigenous and Māori data sovereignty; data and information privacy,
including collective privacy; and Māori and Indigenous privacy perspectives. We examine key features of the Aotearoa privacy context – including the Privacy Act 2020 (NZ) – and consider the implications of te Tiriti o Waitangi and tikanga Māori for alternative privacy approaches. Future options, including legal and extra-legal measures, are proposed.

text: Māori data sovereignty and privacy Privacy is a fundamental human right. One of its most important aspects is information privacy – providing individuals with control over the way in which their personal data is collected, used, disclosed and otherwise handled. Existing information privacy regulation neither recognises nor protects the collective privacy rights of Indigenous peoples. This paper explores Indigenous data
privacy, and the challenges and opportunities, in the context of Aotearoa. It has two aims: to identify gaps in existing data privacy approaches with regards to Indigenous data, and to provide a foundation for progressing alternative privacy paradigms. We argue that while personal data protection is necessary, it is insufficient to meet the needs of Māori and Aotearoa more broadly. In so doing, we draw on three areas of research: Indigenous and Māori data sovereignty; data and information privacy,
including collective privacy; and Māori and Indigenous privacy perspectives. We examine key features of the Aotearoa privacy context – including the Privacy Act 2020 (NZ) – and consider the implications of te Tiriti o Waitangi and tikanga Māori for alternative privacy approaches. Future options, including legal and extra-legal measures, are proposed.

year: 2023