title: Professional competition and cooperation in the digital age: A pilot study of New Zealand practitioners

authors: Toledano, Margalit

subjects: public relations

abstract: From early on, competition between professions has been identified as a major factor in the process of developing public relations as a profession. This paper updates consideration of the influence of inter-profession disputes by examining the interplay between public relations and advertising in the digital age. It attempts to assess how new technologies, especially social media, affect the relationships between them, with particular reference to two aspects: whether social media increases, or decreases, professional cooperation (or competition); and who takes charge in the new communication environment. In researching these issues, which form an important part of the professionalization process of public relations, the paper examined documents and online publications, and undertook interviews and a survey of public relations practitioners in New Zealand. The findings suggest that the responding public relations practitioners were not able to take advantage of the social technology in order to leverage their professional status and improve their position within the organization. In addition, it seems that while advertising and public relations are still looking for ways to benefit from social media, both are still in a process of adaptation.

text: Professional competition and cooperation in the digital age: A pilot study of New Zealand practitioners From early on, competition between professions has been identified as a major factor in the process of developing public relations as a profession. This paper updates consideration of the influence of inter-profession disputes by examining the interplay between public relations and advertising in the digital age. It attempts to assess how new technologies, especially social media, affect the relationships between them, with particular reference to two aspects: whether social media increases, or decreases, professional cooperation (or competition); and who takes charge in the new communication environment. In researching these issues, which form an important part of the professionalization process of public relations, the paper examined documents and online publications, and undertook interviews and a survey of public relations practitioners in New Zealand. The findings suggest that the responding public relations practitioners were not able to take advantage of the social technology in order to leverage their professional status and improve their position within the organization. In addition, it seems that while advertising and public relations are still looking for ways to benefit from social media, both are still in a process of adaptation.

year: 2010

title: Real-time New Zealand sign language translator using convolution neural network

authors: Jayasekera, Mathes Kankanamge Chami

subjects: Sign Language Translator

abstract: Over the past quarter of a century, machine Learning performs an essential role in information technology revolution. From predictive internet web browsing to autonomous vehicles; machine learning has become the heart of all intelligence applications in service today. Image classification through gesture recognition is sub field which has benefited immensely from the existence of this machine learning method. In particular, a subset of Machine Learning known as deep learning has exhibited impressive performance in this regard while outperforming other conventional approaches such as image processing. The advanced Deep Learning architectures come with artificial neural networks particularly convolution neural networks (CNN). Deep Learning has dominated the field of computer vision since 2012; however, a general criticism of this deep learning method is its dependence on large datasets. In order to overcome this criticism, research focusing on discovering data- efficient deep learning methods have been carried out. The foremost finding of the data-efficient deep learning function is a transfer learning technique, which is basically carried out with pre-trained networks. In this research, the InceptionV3 pre trained model has been used to perform the transfer learning method in a convolution neural network to implement New Zealand sign language translator in real-time. 
The focus of this research is to introduce a vision-based application that offers New Zealand sign language translation into text format by recognizing sign gestures to overcome the communication barriers between the deaf community and hearing-unimpaired community in New Zealand. As a byproduct of this research work, a new dataset for New Zealand sign Language alphabet has been created. After training the pre-trained InceptionV3 network with this captured dataset, a prototype for this New Zealand sign language translating system has been created.

text: Real-time New Zealand sign language translator using convolution neural network Over the past quarter of a century, machine Learning performs an essential role in information technology revolution. From predictive internet web browsing to autonomous vehicles; machine learning has become the heart of all intelligence applications in service today. Image classification through gesture recognition is sub field which has benefited immensely from the existence of this machine learning method. In particular, a subset of Machine Learning known as deep learning has exhibited impressive performance in this regard while outperforming other conventional approaches such as image processing. The advanced Deep Learning architectures come with artificial neural networks particularly convolution neural networks (CNN). Deep Learning has dominated the field of computer vision since 2012; however, a general criticism of this deep learning method is its dependence on large datasets. In order to overcome this criticism, research focusing on discovering data- efficient deep learning methods have been carried out. The foremost finding of the data-efficient deep learning function is a transfer learning technique, which is basically carried out with pre-trained networks. In this research, the InceptionV3 pre trained model has been used to perform the transfer learning method in a convolution neural network to implement New Zealand sign language translator in real-time. 
The focus of this research is to introduce a vision-based application that offers New Zealand sign language translation into text format by recognizing sign gestures to overcome the communication barriers between the deaf community and hearing-unimpaired community in New Zealand. As a byproduct of this research work, a new dataset for New Zealand sign Language alphabet has been created. After training the pre-trained InceptionV3 network with this captured dataset, a prototype for this New Zealand sign language translating system has been created.

year: 2021

title: Ethnicity and deliberate self-injury: A review of the literature

authors: Wilson, Cate

subjects: Maori

abstract: Deliberate self-injury is a significant social problem affecting youth in New Zealand. Rates of
hospitalisation for youth (aged 15 to 19) from deliberate self-injury approximate 225 per
100,000. It appears that the rates for Maori and women are significantly higher. From 1987 to
1993, an average of 488 Maori women per 100 000 population have been hospitalised each
year (Ministry of Health: Manatu Hauora, 1996). This paper draws upon both local and
international literature to examine factors underlying this ethnic disparity.
There is a wealth of literature examining risk factors underlying suicidal behaviour as a whole.
Deliberate self-injury is usually assumed to be an adjunct of youth suicide; prevention
strategies are conflated. This paper argues that this assumption is untenable, and in particular,
that prevention strategies designed for youth suicide are problematic in terms of deliberate selfinjury.
While prevention strategies are based upon studies that do not differentiate between
these groups, results will be compromised.

text: Ethnicity and deliberate self-injury: A review of the literature Deliberate self-injury is a significant social problem affecting youth in New Zealand. Rates of
hospitalisation for youth (aged 15 to 19) from deliberate self-injury approximate 225 per
100,000. It appears that the rates for Maori and women are significantly higher. From 1987 to
1993, an average of 488 Maori women per 100 000 population have been hospitalised each
year (Ministry of Health: Manatu Hauora, 1996). This paper draws upon both local and
international literature to examine factors underlying this ethnic disparity.
There is a wealth of literature examining risk factors underlying suicidal behaviour as a whole.
Deliberate self-injury is usually assumed to be an adjunct of youth suicide; prevention
strategies are conflated. This paper argues that this assumption is untenable, and in particular,
that prevention strategies designed for youth suicide are problematic in terms of deliberate selfinjury.
While prevention strategies are based upon studies that do not differentiate between
these groups, results will be compromised.

year: 1999