'I wouldn't say that' : finding a young adult, female voice in a Queensland mining town

This is a practice-led project consisting of a Young Adult novel, Open Cut, and an exegesis, 'I Wouldn't Say That': Finding a Young Adult, Female Voice in a Queensland Mining Town. The thesis investigates the use of first person narration in order to create an immediate engaging, realist Young Adult Fiction. The research design is bound by a feminist interpretative paradigm. The methodology employed is practice-led, auto-ethnography, and participant observation. Particular characteristics of first person narration used in Australian Young Adult Fiction are identified in an analysis of Dust, by Christine Bongers, and Jasper Jones, by Craig Silvey. The exegesis also contains a reflection on the researcher's creative work, and the process used to draft, edit, plot and construct the novel. The research contributes to knowledge in the field of Young Adult Literature because it offers a graphic portrayal of an Australian mining town that has not been heard before.

Business process mining success

Process mining has developed into a popular research discipline and nowadays its associated techniques are widely applied in practice. What is currently ill-understood is how the success of a process mining project can be measured and what the antecedent factors of process mining success are. We consider an improved, grounded understanding of these aspects of value to better manage the effectiveness and efficiency of process mining projects in practice. As such, we advance a model, tailored to the characteristics of process mining projects, which identifies and relates success factors and measures. We draw inspiration from the literature from related fields for the construction of a theoretical, a priori model. That model has been validated and re-specified on the basis of a multiple case study, which involved four industrial process mining projects. The unique contribution of this paper is that it presents the first set of success factors and measures on the basis of an analysis of real process mining projects. The presented model can also serve as a basis for further extension and refinement using insights from additional analyses.

Oscar Pistorius and the death of Reeva Steenkamp : mapping public opinions on Twitter using frame analysis and data mining

This paper uses innovative content analysis techniques to map how the death of Oscar Pistorius' girlfriend, Reeva Steenkamp, was framed on Twitter conversations. Around 1.5 million posts from a two-week timeframe are analyzed with a combination of syntactic and semantic methods. This analysis is grounded in the frame analysis perspective and is different than sentiment analysis. Instead of looking for explicit evaluations, such as “he is guilty” or “he is innocent”, we showcase through the results how opinions can be identified by complex articulations of more implicit symbolic devices such as examples and metaphors repeatedly mentioned. Different frames are adopted by users as more information about the case is revealed: from a more episodic one, highly used in the very beginning, to more systemic approaches, highlighting the association of the event with urban violence, gun control issues, and violence against women. A detailed timeline of the discussions is provided.

‘Our’ community : corporate social responsibility, neoliberalisation, and mining industry community engagement in rural Australia

This paper addresses contemporary neoliberal mobilisations of community undertaken by private corporations. It does so by examining the ways in which the mining industry, empowered through the legitimising framework of corporate social responsibility, is increasingly and profoundly involved in shaping the meaning, practice, and experience of ‘local community’. We draw on a substantial Australian case study, consisting of interviews and document analysis, as a means to examine ‘community-engagement’ practices undertaken by BHP Billiton’s Ravensthorpe Nickel Operation in the Shire of Ravensthorpe in rural Australia. This engagement, we argue, as a process of deepening neoliberalisation simultaneously defines and transforms local community according to the logic of global capital. As such, this study has implications for critical understandings of the intersections among corporate social responsibility, neoliberalisation, community, and capital.

Detecting news topics from microblogs using sequential pattern mining

This thesis presents a sequential pattern based model (PMM) to detect news topics from a popular microblogging platform, Twitter. PMM captures key topics and measures their importance using pattern properties and Twitter characteristics. This study shows that PMM outperforms traditional term-based models, and can potentially be implemented as a decision support system. The research contributes to news detection and addresses the challenging issue of extracting information from short and noisy text.

Mining universities : can we count on the integrity of research coming out of today's mining-funded tertiary sector?

Australia's economic growth and national identity have been widely celebrated as being founded on the nation's natural resources. With the golden era of pastoralism fading into the distance, a renewed love affair with primary industries has been much lauded, particularly by purveyors of neoliberal ideology. The considerable wealth generated by resource extraction has, despite its environmental and social record, proved seductive to the university sector. The mining industry is one of a number of industries and sectors (alongside pharmaceutical, chemical and biotechnological) that is increasingly courting Australian universities. These new public-private alliances are often viewed as the much-needed cash cow to bridge the public funding shortfall in the tertiary sector. However, this trend also raises profound questions about the capacity of public good institutions, as universities were once assumed to be, to maintain institutional independence and academic freedoms.

Mining specific features for acquiring user information needs

Term-based approaches can extract many features in text documents, but most include noise. Many popular text-mining strategies have been adapted to reduce noisy information from extracted features; however, text-mining techniques suffer from low frequency. The key issue is how to discover relevance features in text documents to fulfil user information needs. To address this issue, we propose a new method to extract specific features from user relevance feedback. The proposed approach includes two stages. The first stage extracts topics (or patterns) from text documents to focus on interesting topics. In the second stage, topics are deployed to lower level terms to address the low-frequency problem and find specific terms. The specific terms are determined based on their appearances in relevance feedback and their distribution in topics or high-level patterns. We test our proposed method with extensive experiments in the Reuters Corpus Volume 1 dataset and TREC topics. Results show that our proposed approach significantly outperforms the state-of-the-art models.

Automatic classification of free-text radiology reports to identify limb fractures using machine learning and the SNOMED CT ontology

Objective To develop and evaluate machine learning techniques that identify limb fractures and other abnormalities (e.g. dislocations) from radiology reports. Materials and Methods 99 free-text reports of limb radiology examinations were acquired from an Australian public hospital. Two clinicians were employed to identify fractures and abnormalities from the reports; a third senior clinician resolved disagreements. These assessors found that, of the 99 reports, 48 referred to fractures or abnormalities of limb structures. Automated methods were then used to extract features from these reports that could be useful for their automatic classification. The Naive Bayes classification algorithm and two implementations of the support vector machine algorithm were formally evaluated using cross-fold validation over the 99 reports. Result Results show that the Naive Bayes classifier accurately identifies fractures and other abnormalities from the radiology reports. These results were achieved when extracting stemmed token bigram and negation features, as well as using these features in combination with SNOMED CT concepts related to abnormalities and disorders. The latter feature has not been used in previous works that attempted classifying free-text radiology reports. Discussion Automated classification methods have proven effective at identifying fractures and other abnormalities from radiology reports (F-Measure up to 92.31%). Key to the success of these techniques are features such as stemmed token bigrams, negations, and SNOMED CT concepts associated with morphologic abnormalities and disorders. Conclusion This investigation shows early promising results and future work will further validate and strengthen the proposed approaches.

The mining boom and Western Australia's changing landscape : towards sustainability or business as usual?

The practices and public reputation of mining have been changing over time. In the past, mining operations frequently stood accused of being socially and environmentally disruptive, whereas mining today invests heavily in ‘socially responsible’ and ‘sustainable’ business practices. Changes such as these can be witnessed internationally as well as in places like Western Australia (WA), where the mining sector has matured into an economic pillar of the state, and indeed the nation in the context of the recent resources boom. This paper explores the role of mining in WA, presenting a multi-disciplinary perspective on the sector's contribution to sustainable development in the state. The perspectives offered here are drawn from community-based research and the associated academic literature as well as data derived from government sources and the not-for-profit sector. Findings suggest that despite noteworthy attitudinal and operational improvements in the industry, social, economic and environmental problem areas remain. As mining in WA is expected to grow in the years to come, these problem areas require the attention of business and government alike to ensure the long-term sustainability of development as well as people and place.

The ‘feminine revolution in mining’ : a critique

This paper presents an analysis of media reports of Australian women in mine management. It argues that a dominant storyline in the texts is one of gender change; in fact, a ‘feminine revolution’ is said to have occurred in the mining industry and corporate Australia more generally. Despite this celebratory and transformative discourse the female mine managers interviewed in the media texts seek to distance themselves from women/female identity/femininity and take up a script of gender neutrality. It is demonstrated, however, that this script is saturated with the assumptions and definitions of managerial masculinity.

“Scary” heterosexualities in a rural Australian mining town

This paper draws upon Hubbard's (1999, p. 57) term ‘scary heterosexualities,’ that is non-normative heterosexuality, in the context of the rural drawing on data from fieldwork in the remote Western Australian mining town of Kalgoorlie. Our focus is ‘the skimpie’ – a female barmaid who serves in her underwear and who, in both historical and contemporary times, is strongly associated with rural mining communities. Interviews with skimpies and local residents as well as participant observation reveal how potential fears and anxieties about skimpies are managed. We identify the discursive and spatial processes by which skimpie work is contained in Kalgoorlie so that the potential scariness ‘the skimpie’ represents to the rural is muted and buttressed in terms of a more conventional and less threatening rural heterosexuality.

The Australian mining industry and the ideal mining woman : mobilizing a public business case for gender equality

Despite ongoing ‘boom’ conditions in the Australian mining industry, women remain substantially and unevenly under-represented in the sector, as is the case in other resource-dependent countries. Building on the literature critiquing business-case rationales and strategies as a means to achieve women’s equality in the workplace, we examine the business case for employing more women as advanced by the Australian mining industry. Specifically, we apply a discourse analysis to seven substantial, publically-available documents produced by the industry’s national and state peak organizations between 2005 and 2013. Our study makes two contributions. First, we map the features of the business case at the sectoral rather than firm or workplace level and examine its public mobilization. Second, we identify the construction and deployment of a normative identity – ‘the ideal mining woman’ – as a key outcome of this business-case discourse. Crucially, women are therein positioned as individually responsible for gender equality in the workplace.

An ontology-based service discovery approach for the provisioning of product-service bundles

More and more traditional manufacturing companies form or join inter-organizational networks to bundle their physical products with related services to offer superior value propositions to their customers. Some of these product-related services can be digitized completely and thus fully delivered electronically. Other services require the physical integration of external factors, but can still be coordinated electronically. In both cases companies and consumers face the problem of discovering appropriate product-related service offerings in the network or market. Based on ideas from the web service discovery discipline we propose a meet-in-the-middle approach between heavy-weight semantic technologies and simple boolean search to address this issue. Our approach is able to consider semantic relations in service descriptions and queries and thus delivers better results than syntax-based search. However – unlike most semantic approaches – it does not require the use of any formal language for semantic markup and thus requires less resources and skills for both service providers and consumers. To fully realize the potentials of the proposed approach a domain ontology is needed. In this research-in-progress paper we construct such an ontology for the domain of product-service bundles through analysis and synthesis of related work on service description. This will serve as an anchor for future research to iteratively improve and evaluate the ontology through collaborative design efforts and practical application.

Interpretation of association rules with multi-tier granule mining

This study was a step forward to improve the performance for discovering useful knowledge – especially, association rules in this study – in databases. The thesis proposed an approach to use granules instead of patterns to represent knowledge implicitly contained in relational databases; and multi-tier structure to interpret association rules in terms of granules. Association mappings were proposed for the construction of multi-tier structure. With these tools, association rules can be quickly assessed and meaningless association rules can be justified according to the association mappings. The experimental results indicated that the proposed approach is promising.

Facilitating query decomposition in query language modeling by association rule mining using multiple sliding windows

This paper presents a novel framework to further advance the recent trend of using query decomposition and high-order term relationships in query language modeling, which takes into account terms implicitly associated with different subsets of query terms. Existing approaches, most remarkably the language model based on the Information Flow method are however unable to capture multiple levels of associations and also suffer from a high computational overhead. In this paper, we propose to compute association rules from pseudo feedback documents that are segmented into variable length chunks via multiple sliding windows of different sizes. Extensive experiments have been conducted on various TREC collections and our approach significantly outperforms a baseline Query Likelihood language model, the Relevance Model and the Information Flow model.