Dangers of the after-dark wonderlands : Part B - A review of the impact of the physical environment design on nightclub violence

Violence in nightclubs is a serious problem that has the Australian government launching multimillion dollar drinking campaigns. Research on nightclub violence has focused on identifying contributing social and environmental factors, with many concentrating on a variety of specific issues ranging from financial standpoints with effective target marketing strategies to legal obligations of supplying alcohol and abiding regulatory conditions. Moreover, existing research suggests that there is no single factor that directly affects the rate violence in licensed venues. As detailed in the review paper of Koleczko and Garcia Hansen (2011), there is little research about the physical environment of nightclubs and which specific design properties can be used to determine design standards to ensure/improve the physical design of nightclub environments to reduce patron violence. This current study seeks to address this omission by reporting on a series of interviews with participants from management and design domains. Featured case studies are both located in Fortitude Valley, a Mecca for party-goers and the busiest nightclub district in Queensland. The results and analysis support the conclusions that a number of elements of the physical environment influence elevated patron aggression and assault.

Leaving home for the party on the roof : Brisbane music and musicians

It lies 27°S of the Equator, wrapped uneasily around a wide, muddy river. Three years ago, Brisbane was identified by Billboard Magazine as one of six “hot spots” of independent music in the world. A place to watch. Someone turned a torch on this town, had a quick look, moved on. But this town has always had music in it. Some of it made by me. So, I’m taking my connection with this town, the music and the people, and working it into a contextual historical analysis of the creative lives of Brisbane musicians, and by extension, of Brisbane’s music and Brisbane itself. Talking about what music means to us, how it figures in our lives, and considering the notion, among other factors, of ‘place’ in both our creative practice and creative output. This paper offers an analysis of a particular auto/ethnographic method. How lives are organized and intensified by sounds made and heard in particular social and geographic settings. How music can be the thread which, when pulled, unravels stories, reveals certain truths about musicians and their relationships to one another, to family, to place and to their work.

The link between age, career goals, and adaptive development for work-related learning among local government employees

More recently, lifespan development psychology models of adaptive development have been applied to the workforce to investigate ageing worker and lifespan issues. The current study uses the Learning and Development Survey (LDS) to investigate employee selection and engagement of learning and development goals and opportunities and constraints for learning at work in relation to demographics and career goals. It was found that mature age was associated with perceptions of preferential treatment of younger workers with respect to learning and development. Age was also correlated with several career goals. Findings suggest that younger workers’ learning and development options are better catered for in the workplace. Mature aged workers may compensate for unequal learning opportunities at work by studying for an educational qualification or seeking alternate job opportunities. The desire for a higher level job within the organization or educational qualification was linked to engagement in learning and development goals at work. It is suggested that an understanding of employee perceptions in the workplace in relation to goals and activities may be important in designing strategies to retain workers.

Exponentiated gradient algorithms for conditional random fields and max-margin Markov Networks

Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the log-linear or max-margin objective function; the dual in both the log-linear and max-margin cases corresponds to minimizing a convex function with simplex constraints. We study both batch and online variants of the algorithm, and provide rates of convergence for both cases. In the max-margin case, O(1/ε) EG updates are required to reach a given accuracy ε in the dual; in contrast, for log-linear models only O(log(1/ε)) updates are required. For both the max-margin and log-linear cases, our bounds suggest that the online EG algorithm requires a factor of n less computation to reach a desired accuracy than the batch EG algorithm, where n is the number of training examples. Our experiments confirm that the online algorithms are much faster than the batch algorithms in practice. We describe how the EG updates factor in a convenient way for structured prediction problems, allowing the algorithms to be efficiently applied to problems such as sequence learning or natural language parsing. We perform extensive evaluation of the algorithms, comparing them to L-BFGS and stochastic gradient descent for log-linear models, and to SVM-Struct for max-margin models. The algorithms are applied to a multi-class problem as well as to a more complex large-scale parsing task. In all these settings, the EG algorithms presented here outperform the other methods.

Sparseness vs estimating conditional probabilities : some asymptotic results

One of the nice properties of kernel classifiers such as SVMs is that they often produce sparse solutions. However, the decision functions of these classifiers cannot always be used to estimate the conditional probability of the class label. We investigate the relationship between these two properties and show that these are intimately related: sparseness does not occur when the conditional probabilities can be unambiguously estimated. We consider a family of convex loss functions and derive sharp asymptotic results for the fraction of data that becomes support vectors. This enables us to characterize the exact trade-off between sparseness and the ability to estimate conditional probabilities for these loss functions.

The Bayesian conditional independence model for measurement error: applications in ecology

The measurement error model is a well established statistical method for regression problems in medical sciences, although rarely used in ecological studies. While the situations in which it is appropriate may be less common in ecology, there are instances in which there may be benefits in its use for prediction and estimation of parameters of interest. We have chosen to explore this topic using a conditional independence model in a Bayesian framework using a Gibbs sampler, as this gives a great deal of flexibility, allowing us to analyse a number of different models without losing generality. Using simulations and two examples, we show how the conditional independence model can be used in ecology, and when it is appropriate.

Funding and employment conditions : critical issues for Australian music therapy beyond 2009

As the Australian Journal of Music Therapy celebrates its 20th year of publication, it is evident that the profession of music therapy in Australia, has made substantial progress over these last 20 years. Jobs are regularly advertised on the website, there is a greater public awareness of what music therapy is, there are government recognised salary awards applicable in several states of the country, working conditions have generally improved, and many Australian music therapists are recognised on the international stage as leaders in their field of expertise. You can even go to a party and tell someone you are a music therapist and there is a good chance they will say 'oh yeah, I know someone who does that at the hospital / school / community centre / nursing home' instead of saying 'oh, so like, a what?'. Despite the impressive leaps and bounds that have been made, and the success of many programs in Australia to date, there is still a great deal of room for improvement. What are the critical issues ahead for the development of music therapy in Australia? In particular, how do music therapists develop going forward and secure funding for clinical initiatives? In reflecting on this question, this article identifies two key areas, amongst the many, that can be addressed by music therapists over the next 20 years: funding and employment conditions. Examples from the national early intervention music therapy program 'Sing and Grow' are used to illustrate the potential impact of addressing these two issues on the positive development of the profession into the future.

Qualified indefeasibility and the careless mortgagee

In recent years a great deal of case law has been generated in relation to mortgages where the mortgagee has not engaged in adequate identity verification of the mortgagor and the mortgage has subsequently been found to be forged. As a result, careless mortgagee provisions operate in Queensland as an exception to indefeasibility. Similar provisions are expected to commence soon in New South Wales. This article examines the mortgagee’s position with the benefit of indefeasibility and then considers the impact of the careless mortgagee provisions on the rights of a mortgagee under a forged mortgage, concluding that the provisions significantly change the dynamic between a registered mortgagee and registered owner who has not signed the mortgage. These provisions appear to give the mortgagee a conditional indefeasibility, with the intention of reducing the State’s exposure to the payment of compensation in the case of identity fraud. They are however, more successful in the case of forgery by a third party rather than forgery by a co-owner.

Designing university, government and industry community engagement to build informed and effective interventions in the area of road safety

Individuals, community organisations and industry have always been involved to varying degrees in efforts to address the Queensland road toll. Traditionally, road crash prevention efforts have been led by state and local government organisations. While community and industry groups have sometimes become involved (e.g. Driver Reviver campaign), their efforts have largely been uncoordinated and under-resourced. A common strength of these initiatives lies in the energy, enthusiasm and persistence of community-based efforts. Conversely, a weakness has sometimes been the lack of knowledge, awareness or prioritisation of evidence-based interventions or their capacity to build on collaborative efforts. In 2000, the Queensland University of Technology’s Centre for Accident Research and Road Safety – Queensland (CARRS-Q) identified this issue as an opportunity to bridge practice and research and began acknowledging a selection of these initiatives, in partnership with the RACQ, through the Queensland Road Safety Awards program. After nine years it became apparent there was need to strengthen this connection, with the Centre establishing a Community Engagement Workshop in 2009 as part of the overall Awards program. With an aim of providing community participants opportunities to see, hear and discuss the experiences of others, this event was further developed in 2010, and with the collaboration of the Queensland Department of Transport and Main Roads, the RACQ, Queensland Police Service and Leighton Contractors Pty Ltd, a stand-alone Queensland Road Safety Awards Community Engagement Workshop was held in 2010. Each collaborating organisation recognised a need to mobilise the community through effective information and knowledge sharing, and recognised that learning and discussion can influence lasting behaviour change and action in this often emotive, yet not always evidence-based, area. This free event featured a number of speakers representing successful projects from around Australia and overseas. Attendees were encouraged to interact with the speakers, to ask questions, and most importantly, build connections with other attendees to build a ‘community road safety army’ all working throughout Australia on projects underpinned by evaluated research. The workshop facilitated the integration of research, policy and grass-roots action enhancing the success of community road safety initiatives. For collaboration partners, the event enabled them to transfer their knowledge in an engaged approach, working within a more personal communication process. An analysis of the success factors for this event identified openness to community groups and individuals, relevance of content to local initiatives, generous support with the provision of online materials and ongoing communication with key staff members as critical and supports the view that the university can directly provide both the leadership and the research needed for effective and credible community-based initiatives to address injury and death on the roads.

An epistemic community as influencer and implementer in local government accounting in Australia

This paper adopts an epistemic community framework to explicate the dual role of epistemic communities as influencers of accounting policy within regulatory space and as implementers who effect change within the domain of accounting. The context is the adoption and implementation of fair value accounting within local government in New South Wales (NSW). The roles and functions of Australian local government are extensive, and include the development and maintenance of infrastructure, provision of recreational facilities, certain health and community services, buildings, cultural facilities, and in some cases, water and sewerage (Australian Local Government Association, 2009). The NSW state Department of Local Government (DLG) is responsible for legislation and policy development to ensure that local councils are able to deliver ‘quality services to their communities in a sustainable manner’ (DLG, 2008c). These local councils receive revenue from various sources including property rates, government grants and user-pays service provision. In July 2006 the DLG issued Circular 06-453 to councils (DLG, 2006c), mandating the staged adoption of fair value measurement of infrastructure assets. This directive followed the policy of NSW State Treasury (NSW Treasury, 2007),4 and an independent inquiry into the financial sustainability of local councils (LGSA, 2006). It was an attempt to resolve the inconsistency in public sector asset valuation in NSW Local Governments, and to provide greater usefulness and comparability of financial statements.5 The focus of this study is the mobilization of accounting change by the DLG within this wider political context. When a regulatory problem arises, those with political power seek advice from professionals with relevant skill and expertise (Potter, 2005). This paper explores the way in which professionals diffuse accounting ‘problems’ and the associated accounting solutions ‘across time and space’ (Potter, 2005, p. 277). The DLG’s fair value accounting policy emanated from a ‘regulatory space’ (Hancher and Moran, 1989)6 as a result of negotiations between many parties, including accounting and finance professionals. Operating within the local government sector, these professionals were identified by the DLG as being capable of providing helpful input. They were also responsible for the implementation of the new olicy within local councils. Accordingly they have been dentified as an pistemic community with the ability to ranslate regulatory power by changing he domain of ccounting (Potter, 2005, p. 278).7 The paper is organised as follows. The background to the LG’s decision to require the introduction of fair value accounting for infrastructure assets is explored. Following this, the method of the study is described, and the epistemic community framework outlined. In the next sections, evidence of the influencing and implementing roles of epistemic groups is provided. Finally, conclusions are drawn about the significance of these groups both within regulatory space in developing accounting regulation, and in embedding change within the domain of accounting.

A continuous time model for election timing

We consider a continuous time model for election timing in a Majoritarian Parliamentary System where the government maintains a constitutional right to call an early election. Our model is based on the two-party-preferred data that measure the popularity of the government and the opposition over time. We describe the poll process by a Stochastic Differential Equation (SDE) and use a martingale approach to derive a Partial Differential Equation (PDE) for the government’s expected remaining life in office. A comparison is made between a three-year and a four-year maximum term and we also provide the exercise boundary for calling an election. Impacts on changes in parameters in the SDE, the probability of winning the election and maximum terms on the call exercise boundaries are discussed and analysed. An application of our model to the Australian Federal Election for House of Representatives is also given.

An infinite-horizon robust filter for uncertain hidden Markov Models with conditional relative entropy constraints

We consider a robust filtering problem for uncertain discrete-time, homogeneous, first-order, finite-state hidden Markov models (HMMs). The class of uncertain HMMs considered is described by a conditional relative entropy constraint on measures perturbed from a nominal regular conditional probability distribution given the previous posterior state distribution and the latest measurement. Under this class of perturbations, a robust infinite horizon filtering problem is first formulated as a constrained optimization problem before being transformed via variational results into an unconstrained optimization problem; the latter can be elegantly solved using a risk-sensitive information-state based filtering.