956 resultados para Arc
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Childcare workers play a significant role in the learning and development of children in their care. This has major implications for the training of workers. Under new reforms of the childcare industry the Australian government now requires all workers to obtain qualifications from a vocational education and training provider (eg. Technical and Further Education) or university. Effective models of employment-based training are critical to provide training to highly competent workers. This paper presents findings from a study that examined current and emerging models of employment-based training in the childcare sector, particularly at the Diploma level. Semi-structured interviews were conducted with a sample of 16 participants who represented childcare directors, employers, and workers located in childcare services in urban, regional and remote locations in the State of Queensland. The study proposes a ‘best-fit’ employment-based training approach that is characterised by a compendium of five models instead of a ‘one size fits all’. Issues with successful implementation of the EBT models are also discussed
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How is contemporary culture 'framed' - understood, promoted, dissected and defended - in the new approaches being employed in university education today? How do these approaches compare with those seen in the public policy process? What are the implications of these differences for future directions in theory, education, activism and policy? Framing Culture looks at cultural and media studies, which are rapidly growing fields through which students are introduced to contemporary cultural industries such as television, film and video. It compares these approaches with those used to frame public policy and finds a striking lack of correspondence between them. Issues such as Australian content on commercial television and in advertising, new technologies and new media, and violence in the media all highlight the gap between contemporary cultural theories and the way culture and communications are debated in public policy. The reasons for this gap must be investigated before closer relations can be established. Framing Culture brings together cultural studies and policy studies in a lively and innovative way. It suggests avenues for cultural activism that have been neglected in cultural theory and practice, and it will provoke debates which are long overdue.
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This book addresses current debates about globalization and culture by tracing the emergence of Australia as a significant exporter of television to the world market. The authors investigate why Australian programs have found international popularity. The book describes the Australian industry and the international television marketplace. It also examines the impact of Australian programs on the television cultures of the importing countries. The authors outline policy implications and speculate on future directions of Australian television.
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Over recent decades, the flow of television programmes and services between nations has prompted concerns about `Cultural Imperialism', the idea that the powerful metropolitan nations at the centre of the world system are breaking down the integrity and autonomy of the peripheral countries. New Patterns in Global Television challenges that notion by showing that some of the countries outside the traditionally dominant centres have now developed strong television industries of their own, and have been expanding into regional markets, especially - but not exclusively - where linguistic and cultural similarities exist. This book brings together contributions from specialist researchers on the most dynamic of these regions: Latin America, India, the Middle East, Greater China and, in the English-speaking world, Canada and Australia. It provides the first comprehensive overview of the new patterns of flow in international television programme exchange and service provision in the satellite era, patterns unrecognised by the perspective of the prevailing theoretical orthodoxies in international communication research and policy.
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This project utilises creative practice as research, and involves writing and discussing four sample episodes of a proposed six-part dramatic, black-comedy1 television mini-series titled The New Lows. Combined, the creative project and accompanying exegesis seeks to illuminate and interrogate some of the inherent concerns, pitfalls and politics encountered in writing original Asian-Australian characters for television. Moreover, this thesis seeks to develop and deliberate on characters that would expand, shift and extend concepts of stereotyping and authenticity as they are used in creative writing for television. The protagonists of The New Lows are the contemporary and dysfunctional Asian-Australian Lo family: the Hong Kong immigrants John and Dorothy, and their Australian-born children Wendy, Simon and Tommy. Collectively, they struggle to manage the family business: a decaying suburban Chinese restaurant called Sunny Days, which is stumbling towards imminent commercial death. At the same time, each of the characters must negotiate their own personal catastrophes, which they hide from fellow family members out of shame and fear. Although there is a narrative arc to the series, I have also endeavoured to write each episode as a selfcontained story. Written alongside the creative works is an exegetical component. Through the paradigm of Asian-Australian studies, the exegesis examines the writing process and narrative content of The New Lows, alongside previous representations of Asians on Australian and international television and screen. Concepts discussed include stereotype, ethnicity, otherness, hybridity and authenticity. However, the exegesis also seeks to question the dominant cultural paradigms through which these issues are predominantly discussed. These investigations are particularly relevant, since The New Lows draws upon a suite of characters commonly considered to be stereotypical in Asian-Australian representations.
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It is a big challenge to clearly identify the boundary between positive and negative streams. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on RCV1, and substantial experiments show that the proposed approach achieves encouraging performance.
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Over the years, people have often held the hypothesis that negative feedback should be very useful for largely improving the performance of information filtering systems; however, we have not obtained very effective models to support this hypothesis. This paper, proposes an effective model that use negative relevance feedback based on a pattern mining approach to improve extracted features. This study focuses on two main issues of using negative relevance feedback: the selection of constructive negative examples to reduce the space of negative examples; and the revision of existing features based on the selected negative examples. The former selects some offender documents, where offender documents are negative documents that are most likely to be classified in the positive group. The later groups the extracted features into three groups: the positive specific category, general category and negative specific category to easily update the weight. An iterative algorithm is also proposed to implement this approach on RCV1 data collections, and substantial experiments show that the proposed approach achieves encouraging performance.
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Dealing with the ever-growing information overload in the Internet, Recommender Systems are widely used online to suggest potential customers item they may like or find useful. Collaborative Filtering is the most popular techniques for Recommender Systems which collects opinions from customers in the form of ratings on items, services or service providers. In addition to the customer rating about a service provider, there is also a good number of online customer feedback information available over the Internet as customer reviews, comments, newsgroups post, discussion forums or blogs which is collectively called user generated contents. This information can be used to generate the public reputation of the service providers’. To do this, data mining techniques, specially recently emerged opinion mining could be a useful tool. In this paper we present a state of the art review of Opinion Mining from online customer feedback. We critically evaluate the existing work and expose cutting edge area of interest in opinion mining. We also classify the approaches taken by different researchers into several categories and sub-categories. Each of those steps is analyzed with their strength and limitations in this paper.
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This paper investigates self–Googling through the monitoring of search engine activities of users and adds to the few quantitative studies on this topic already in existence. We explore this phenomenon by answering the following questions: To what extent is the self–Googling visible in the usage of search engines; is any significant difference measurable between queries related to self–Googling and generic search queries; to what extent do self–Googling search requests match the selected personalised Web pages? To address these questions we explore the theory of narcissism in order to help define self–Googling and present the results from a 14–month online experiment using Google search engine usage data.