Empowerment as a framework for Indigenous workforce development and organisational change

Relative powerlessness resulting from colonial dispossession and associated passive welfare policies has long been recognised as a critical factor influencing the health and wellbeing of Indigenous Australians, yet it is hard to find well-evaluated health and social interventions that take an explicit empowerment approach. This paper presents the findings of a Family Wellbeing Empowerment programme pilot delivered to Cairns Region Department of Families Indigenous youth workers and family and community workers in 2003/2004. The aim of the pilot was to build the capacity of these workers to address personal and professional issues as a basis for providing better support for their clients. The pilot demonstrated the effectiveness of the programme as a tool for worker empowerment and, to a lesser degree, organisational change.

High-Order Concept Associations Mining and Inferential Language Modeling for Online Review Spam Detection

Despite many incidents about fake online consumer reviews have been reported, very few studies have been conducted to date to examine the trustworthiness of online consumer reviews. One of the reasons is the lack of an effective computational method to separate the untruthful reviews (i.e., spam) from the legitimate ones (i.e., ham) given the fact that prominent spam features are often missing in online reviews. The main contribution of our research work is the development of a novel review spam detection method which is underpinned by an unsupervised inferential language modeling framework. Another contribution of this work is the development of a high-order concept association mining method which provides the essential term association knowledge to bootstrap the performance for untruthful review detection. Our experimental results confirm that the proposed inferential language model equipped with high-order concept association knowledge is effective in untruthful review detection when compared with other baseline methods.

Economic prosperity, suburbanization, and the creative workforce : findings from Australian suburban communities

This paper considers the changing relationship between economic prosperity and Australian suburbs, noting that what has been termed “the first suburban nation” in experiencing an intensification of suburban growth in the 2000s, in the context of economic globalization. The paper reports on a three-year Australian Research Council funded project into “Creative Suburbia”, identifying the significant percentage of the creative industries workforce who live in suburban areas. Drawing on case studies from suburbs in the Australian cities of Brisbane and Melbourne, it notes the contrasts between the experience of these workers, who are generally positive towards suburban life, and the underlying assumptions of “creative cities” policy discourse that such workers prefer to be concentrated in high density inner urban creative clusters.

Auckland's creative workforce report 2010

The report aims to improve the understanding of Auckland’s creative employment by applying a proven methodology to detailed employment and earnings data from recent NZ Censuses. The approach analyses creative employment based on the occupations of those employed within their industry of employment. The dual dimensions allow a more nuanced understanding than the traditional approaches of employment within creative industries or employment within creative occupations.

New variational Bayesian approaches for statistical data mining : with applications to profiling and differentiating habitual consumption behaviour of customers in the wireless telecommunication industry

This thesis investigates profiling and differentiating customers through the use of statistical data mining techniques. The business application of our work centres on examining individuals’ seldomly studied yet critical consumption behaviour over an extensive time period within the context of the wireless telecommunication industry; consumption behaviour (as oppose to purchasing behaviour) is behaviour that has been performed so frequently that it become habitual and involves minimal intentions or decision making. Key variables investigated are the activity initialised timestamp and cell tower location as well as the activity type and usage quantity (e.g., voice call with duration in seconds); and the research focuses are on customers’ spatial and temporal usage behaviour. The main methodological emphasis is on the development of clustering models based on Gaussian mixture models (GMMs) which are fitted with the use of the recently developed variational Bayesian (VB) method. VB is an efficient deterministic alternative to the popular but computationally demandingMarkov chainMonte Carlo (MCMC) methods. The standard VBGMMalgorithm is extended by allowing component splitting such that it is robust to initial parameter choices and can automatically and efficiently determine the number of components. The new algorithm we propose allows more effective modelling of individuals’ highly heterogeneous and spiky spatial usage behaviour, or more generally human mobility patterns; the term spiky describes data patterns with large areas of low probability mixed with small areas of high probability. Customers are then characterised and segmented based on the fitted GMM which corresponds to how each of them uses the products/services spatially in their daily lives; this is essentially their likely lifestyle and occupational traits. Other significant research contributions include fitting GMMs using VB to circular data i.e., the temporal usage behaviour, and developing clustering algorithms suitable for high dimensional data based on the use of VB-GMM.

Health Librarianship Workforce and Education : Research to plan the future. Final Report.

In late 2009, Health Libraries Australia (HLA) received a small grant to undertake a national research project to determine the future requirements for health librarians in the workforce in Australia and develop a structured, modular education framework (post-graduate qualification and continuing professional development structure) to meet these requirements. The main objective was to consider the education and professional development framework that would ensure that health librarians have a clearly defined scope of practice and the specific competency based knowledge and skills that enable them to contribute to the design and delivery of high quality health services in this country. The final report presents a detailed discussion of the changing Australian healthcare environment and the resulting impact on the health library sector, as well as an overview of international trends in health libraries and the implications for Australian health librarianship education. The research methodology is outlined, followed by an analysis of the findings from the two surveys with health librarians and health library managers and the semi-structured interviews conducted with employers. The Medical Library Association (MLA) in the United States had developed a policy document detailing the competencies required by health librarians. It was found that the MLA competencies represented an accepted professional framework of skills which could be used objectively in the survey instrument to measure the areas of professional knowledge and responsibilities that were relevant in the current workplace, and to identify how these requirements might change in the next three to five years. The research results underscore the imperative for health librarians to engage in regular, relevant professional development activities that will enable them to stay abreast with the rapid contextual changes impacting on their practice. In order to be accepted as key members of the multi-disciplinary health professional team, it is strongly believed that health librarians should commit to establishing the mechanisms for specialist certification maintained through compulsory CPD in an ongoing three-year cycle of revalidation. This development would align ALIA and health librarians with other health sector professional associations which are responsible for the self regulation of entry to and continuation in their profession.

Non-redundant sequential association rule mining based on closed sequential patterns

In many applications, e.g., bioinformatics, web access traces, system utilisation logs, etc., the data is naturally in the form of sequences. People have taken great interest in analysing the sequential data and finding the inherent characteristics or relationships within the data. Sequential association rule mining is one of the possible methods used to analyse this data. As conventional sequential association rule mining very often generates a huge number of association rules, of which many are redundant, it is desirable to find a solution to get rid of those unnecessary association rules. Because of the complexity and temporal ordered characteristics of sequential data, current research on sequential association rule mining is limited. Although several sequential association rule prediction models using either sequence constraints or temporal constraints have been proposed, none of them considered the redundancy problem in rule mining. The main contribution of this research is to propose a non-redundant association rule mining method based on closed frequent sequences and minimal sequential generators. We also give a definition for the non-redundant sequential rules, which are sequential rules with minimal antecedents but maximal consequents. A new algorithm called CSGM (closed sequential and generator mining) for generating closed sequences and minimal sequential generators is also introduced. A further experiment has been done to compare the performance of generating non-redundant sequential rules and full sequential rules, meanwhile, performance evaluation of our CSGM and other closed sequential pattern mining or generator mining algorithms has also been conducted. We also use generated non-redundant sequential rules for query expansion in order to improve recommendations for infrequently purchased products.

Regulating greenhouse gas emissions from coal mining activities in the context of climate change

Current approaches to the regulation of coal mining activities in Australia have facilitated the extraction of substantial amounts of coal and coal seam gas. The regulation of coal mining activities must now achieve the reduction or mitigation of greenhouse gas emissions in order to address the challenge of climate change and achieve ecologically sustainable development. Several legislative mechanisms currently exist which appear to offer the means to bring about the reduction or mitigation of greenhouse gas emissions from coal mining activities, yet Australia’s emissions from coal mining continue to rise. This article critiques these existing legislative mechanisms and presents recommendations for reform.

Identifying differences in safe roads and crash prone roads using clustering data mining

Road asset managers are overwhelmed with a high volume of raw data which they need to process and utilise in supporting their decision making. This paper presents a method that processes road-crash data of a whole road network and exposes hidden value inherent in the data by deploying the clustering data mining method. The goal of the method is to partition the road network into a set of groups (classes) based on common data and characterise the class crash types to produce a crash profiles for each cluster. By comparing similar road classes with differing crash types and rates, insight can be gained into these differences that are caused by the particular characteristics of their roads. These differences can be used as evidence in knowledge development and decision support.