'Putting up your Dukes': Statues, social memory and Duke Paoa Kahanamoku

Public statues that commemorate the lives and achievements of athletes are pervasive and influential forms of social memory in Western societies. Despite this important nexus between cultural practice and history making, there is a relative void of critical studies of statuary dedicated to athletes. This article will attempt to contribute to a broader understanding in this area by considering a bronze statue of Duke Paoa Kahanamoku, the Hawaiian Olympian, swimmer and surfer, at Waikīkī, Hawaii. This prominent monument demonstrates the processes of remembering and forgetting that are integral to acts of social memory. In this case, Kahanamoku's identity as a surfer is foregrounded over his legacy as a swimmer. The distillation and use of Kahanamoku's memory in this representation is enmeshed in deeper cultural forces about Hawaii's identity. Competing meanings of the statue's symbolism indicate its role as a 'hollow icon', and illustrate the way that apparently static objects representing the sporting past are in fact objects of the present.

Balancing curriculum processes and content in a project centred curriculum - In pursuit of graduate attributes

Chemical engineering education is challenged around the world by demands and rapid changes encompassing a wide range of technical and social drivers. Graduates must be prepared for practice in increasingly diverse workplace environments in which generic or transferable attributes such as communication and teamwork together with technical excellence are mandated by prospective employers and society at large. If academe is to successfully deliver on these graduate attributes, effective curriculum design needs to include appropriate educational processes as well as course content. Conventional teacher centred approaches, stand-alone courses and retro-fitted remedial modules have not delivered the desired outcomes. Development of the broader spectrum of attributes is more likely when students are engaged with realistic and relevant experiences that demand the integration and practice of these attributes in contexts that the students find meaningful. This paper describes and evaluates The University of Queensland's Project Centred Curriculum in Chemical Engineering (PCC), a programme-wide approach to meeting these requirements. PCC strategically integrates project-based learning with more traditional instruction. Data collected shows improved levels of student attainment of generic skills with institutional and nationally benchmarked indicators showing significant increases in student perceptions of teaching quality, and overall satisfaction with the undergraduate experience. Endorsements from Australian academic, professional and industry bodies also support the approach as more effectively aligning engineering education with professional practice requirements.

Social capital society and peace: Reflections on conflict transformation in the Philippines

Civil society is recognised as comprising complex and multifaceted entities, resilient to and yet responsive to both the state apparatus and global market processes. Civil society in the Philippines, long regarded as one of the most vibrant, diverse and innovative in Asia, has emerged as a significant actor in the field of conflict resolution and peace-building. In thinking about the work of peace, this paper engages with the effectiveness of civil society in mobilising societal awareness for a ‘just and lasting peace’ in the southern Philippines. Shaped by development paradigms that privilege concepts such as social capital, the paper aims to interrogate how such concepts situated within the development–security nexus proposed by the Philippine government and funding agencies have influenced conflict-transformation initiatives in Mindanao, Philippines.

Asymptotic theory of time varying networks with memory and heterogeneous activation pattern

In this thesis work we develop a new generative model of social networks belonging to the family of Time Varying Networks. The importance of correctly modelling the mechanisms shaping the growth of a network and the dynamics of the edges activation and inactivation are of central importance in network science. Indeed, by means of generative models that mimic the real-world dynamics of contacts in social networks it is possible to forecast the outcome of an epidemic process, optimize the immunization campaign or optimally spread an information among individuals. This task can now be tackled taking advantage of the recent availability of large-scale, high-quality and time-resolved datasets. This wealth of digital data has allowed to deepen our understanding of the structure and properties of many real-world networks. Moreover, the empirical evidence of a temporal dimension in networks prompted the switch of paradigm from a static representation of graphs to a time varying one. In this work we exploit the Activity-Driven paradigm (a modeling tool belonging to the family of Time-Varying-Networks) to develop a general dynamical model that encodes fundamental mechanism shaping the social networks' topology and its temporal structure: social capital allocation and burstiness. The former accounts for the fact that individuals does not randomly invest their time and social interactions but they rather allocate it toward already known nodes of the network. The latter accounts for the heavy-tailed distributions of the inter-event time in social networks. We then empirically measure the properties of these two mechanisms from seven real-world datasets and develop a data-driven model, analytically solving it. We then check the results against numerical simulations and test our predictions with real-world datasets, finding a good agreement between the two. Moreover, we find and characterize a non-trivial interplay between burstiness and social capital allocation in the parameters phase space. Finally, we present a novel approach to the development of a complete generative model of Time-Varying-Networks. This model is inspired by the Kaufman's adjacent possible theory and is based on a generalized version of the Polya's urn. Remarkably, most of the complex and heterogeneous feature of real-world social networks are naturally reproduced by this dynamical model, together with many high-order topological properties (clustering coefficient, community structure etc.).

Bayesian classification with Gaussian processes

We consider the problem of assigning an input vector bfx to one of m classes by predicting P(c|bfx) for c = 1, ldots, m. For a two-class problem, the probability of class 1 given bfx is estimated by s(y(bfx)), where s(y) = 1/(1 + e^-y). A Gaussian process prior is placed on y(bfx), and is combined with the training data to obtain predictions for new bfx points. We provide a Bayesian treatment, integrating over uncertainty in y and in the parameters that control the Gaussian process prior; the necessary integration over y is carried out using Laplace's approximation. The method is generalized to multi-class problems (m >2) using the softmax function. We demonstrate the effectiveness of the method on a number of datasets.

Sparse on-line Gaussian processes

We develop an approach for sparse representations of Gaussian Process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations for large data sets. The method is based on a combination of a Bayesian online algorithm together with a sequential construction of a relevant subsample of the data which fully specifies the prediction of the GP model. By using an appealing parametrisation and projection techniques that use the RKHS norm, recursions for the effective parameters and a sparse Gaussian approximation of the posterior process are obtained. This allows both for a propagation of predictions as well as of Bayesian error measures. The significance and robustness of our approach is demonstrated on a variety of experiments.

Sparse on-line Gaussian processes

We develop an approach for sparse representations of Gaussian Process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations for large data sets. The method is based on a combination of a Bayesian online algorithm together with a sequential construction of a relevant subsample of the data which fully specifies the prediction of the GP model. By using an appealing parametrisation and projection techniques that use the RKHS norm, recursions for the effective parameters and a sparse Gaussian approximation of the posterior process are obtained. This allows both for a propagation of predictions as well as of Bayesian error measures. The significance and robustness of our approach is demonstrated on a variety of experiments.

Sales force social exchange in problem resolution situations

While sales managers spend much of their time resolving sales force-related problems, existing theory offers little insight into the social exchange processes which occur in problem resolution situations. Using a qualitative inquiry method rooted in grounded theory, we uncover three key social exchange contributions used by sales managers when dealing with problem situations in the sales force: sales manager responsiveness, caring, and aggressiveness. We then show that the extent to which managers use these exchange contributions in problem situations is a function of manager characteristics, problem-specific characteristics, and the situational context. We also show that the extent to which managers invest in these three social exchange contributions has implications for the quality for the interpersonal relationships between salespeople and their managers, and for the effectiveness of problem resolution activity.

Majority versus minority influence, message processing and attitude change:the Source-Context-Elaboration Model

This chapter examines the contexts in which people will process more deeply, and therefore be more influenced by, a position that is supported by either a numerical majority or minority. The chapter reviews the major theories of majority and minority influence with reference to which source condition is associated with most message processing (and where relevant, the contexts under which this occurs) and experimental research examining these predictions. The chapter then presents a new theoretical model (the source-context-elaboration model, SCEM) that aims to integrate the disparate research findings. The model specifies the processes underlying majority and minority influence, the contexts under which these processes occur and the consequences for attitudes changed by majority and minority influence. The chapter then describes a series of experiments that address each of the aspects of the theoretical model. Finally, a range of research-related issues are discussed and future issues for the research area as a whole are considered.

The effects of caffeine consumption on direct and indirect majority and minority influence

A study is reported that examines the effect of caffeine consumption on majority and minority influence. In a double blind procedure, 72 participants consumed an orange drink, which either contained caffeine (3.5mg per kilogram of body weight) or did not (placebo). After a 40-minute delay, participants read a counter-attitudinal message (antivoluntary euthanasia) endorsed by either a numerical majority or minority. Both direct (message issue, i.e., voluntary euthanasia) and indirect (message issue-related, i.e., abortion) change was assessed by attitude scales completed before and after exposure to the message. In the placebo condition, the findings replicated the predictions of Moscovici's (1980) conversion theory; namely, majorities leading to compliance (direct influence) and minorities leading to conversion (indirect influence). When participants had consumed caffeine, majorities not only led to more direct influence than in the placebo condition but also to indirect influence. Minorities, by contrast, had no impact on either level of influence. The results suggest that moderate levels of caffeine increase systematic processing of the message but the consequences of this vary for each source. When the source is a majority there was increased indirect influence while for a minority there was decreased indirect influence. The results show the need to understand how contextual factors can affect social influence processes.