Big Society as Big Government: Cameron's Governmentality Agenda

Cameron’s flagship policy of the ‘Big Society’ rests on a society/government dichotomy, diagnosing a ‘broken society’ caused by ‘big government’ having assumed the role communities once played. The remedy is greater social responsibility and the ‘Big Society’. This article argues that the dichotomy is
deceptive. We aim to show that the Big Society is big government, as it employs techniques for managing the conduct of individuals and communities such that the mentality of government, far from being removed or reduced, is bettered and made more efficient. To illustrate this, we explore two major initiatives: the National Citizen Service and the Community Resilience programme. These
projects demonstrate how practices of informing and guiding the conduct of individuals both produce agents and normalise certain values, resulting in the population being better known and controlled. Thus, far from lessening government and empowering people, the Big Society extends governmentality
throughout the social body.

Peer tutoring in reading in Scotland:Thinking big

A large-scale randomised-controlled trial of reading tutoring in 80 schools in Scotland used the Paired Reading (PR) technique. On long-term evaluation, cross-age PR was significantly better than regular teaching, but same-age was not. On short-term evaluation, PR pupils did significantly better than control pupils in both years, and cross-age and same-age were similarly effective. Low socio-economic pupils, lower reading ability pupils, girls and reading with maths tutoring groups did significantly better. Implementation was good in parts, but some important aspects of technique were rare. Reading gains were significantly greater for those with mistakes about every 2 minutes and those who stopped reading to talk every 5 to 7 minutes. Significant gains in self-esteem were seen in same-age and cross-age groups, and for tutees and tutors, but not for controls. The relationship of achievement gain to quality of technique and socio-emotional gains is discussed, with implications for practice. Copyright © 2011 UKLA.

'Big, strong and healthy'. Young children's identification of food and drink that contribute to healthy growth

Growing awareness of the importance of healthy diet in early childhood makes it important to chart the development of children's understanding of food and drink. This study aimed to document young children's evaluation of food and drink as healthy, and to explore relationships with socioeconomic status, family eating habits, and children's television viewing. Data were gathered from children aged 3-5. years (. n=. 172) in diverse socioeconomic settings in Ireland, and from their parents. Results demonstrated that children had very high levels of ability to identify healthy foods as important for growth and health, but considerably less ability to reject unhealthy items, although knowledge of these increased significantly between ages 3 and 5. Awareness of which foods were healthy, and which foods were not, was not related to family socioeconomic status, parent or child home eating habits, or children's television viewing. Results highlighted the importance of examining young children's response patterns, as many of the youngest showed a consistent 'yes bias'; however, after excluding these responses, the significant findings remained. Findings suggest it is important to teach children about less healthy foods in the preschool years. © 2013 Elsevier Ltd.

That Big Swim

A dramatic monologue. Commissioned by the Legacy Trust UK, as part of their mission to create a cultural legacy for the Olympics 2012. It was a collaboration with composer Christopher Norby.

Big Ears – sonic art for public ears: Reflections on collaborative training

This text critically reflects on the higher education public engagement training program, entitled ‘Big Ears – sonic art for public ears’. The authors detail the objectives and aims as well as the benefits of this initiative for the enhancement of the student learning experience. We consider Schmidt’s (Schmidt, 2012) notion of mis-listening and Christopher Small’s concept of ‘musicking’ (Small, 1998), and develop a critical argument on how public engagement has changed researchers’ views and attitudes about their own research. The text explores how the creative interaction with a young audience has had great impact on the students’ learning experience as well as on their employability/transferable skills, because Big Ears stresses the importance of pulling practice as research away from the academic argument of why artists should be supported inside an institution, and into the realm of the real – what to do when making art, how to make it relevant and applicable to audiences.

Gerodontology--how big is the challenge in Ireland?

Population trends suggest that the Irish population is ageing, and that this population will have substantial treatment needs. These patients will be better informed than previous generations, and will demand treatment aimed at preserving a natural dentition. This will impact upon delivery of oral healthcare and manpower planning needs to consider how to address the increased demand for dental care. Poor oral health is associated with systemic health problems, including cardiovascular disease, respiratory disease and diabetes mellitus. It also has a negative impact upon quality of life, and the World Health Organisation has encouraged public healthcare administrators and decision makers to design effective and affordable strategies for better oral health and quality of life of older adults, which, in turn, are integrated into general health management programmes. Treatment concepts such as minimally invasive dentistry and the shortened dental arch concept are discussed in the context of these demographic changes and recommendations.

Towards a general framework for social media research using big data

The increasing adoption of cloud computing, social networking, mobile and big data technologies provide challenges and opportunities for both research and practice. Researchers face a deluge of data generated by social network platforms which is further exacerbated by the co-mingling of social network platforms and the emerging Internet of Everything. While the topicality of big data and social media increases, there is a lack of conceptual tools in the literature to help researchers approach, structure and codify knowledge from social media big data in diverse subject matter domains, many of whom are from nontechnical disciplines. Researchers do not have a general-purpose scaffold to make sense of the data and the complex web of relationships between entities, social networks, social platforms and other third party databases, systems and objects. This is further complicated when spatio-temporal data is introduced. Based on practical experience of working with social media datasets and existing literature, we propose a general research framework for social media research using big data. Such a framework assists researchers in placing their contributions in an overall context, focusing their research efforts and building the body of knowledge in a given discipline area using social media data in a consistent and coherent manner.

Supervised Aggregative Feature Extraction for Big Data Time Series Regression

In many applications, and especially those where batch processes are involved, a target scalar output of interest is often dependent on one or more time series of data. With the exponential growth in data logging in modern industries such time series are increasingly available for statistical modeling in soft sensing applications. In order to exploit time series data for predictive modelling, it is necessary to summarise the information they contain as a set of features to use as model regressors. Typically this is done in an unsupervised fashion using simple techniques such as computing statistical moments, principal components or wavelet decompositions, often leading to significant information loss and hence suboptimal predictive models. In this paper, a functional learning paradigm is exploited in a supervised fashion to derive continuous, smooth estimates of time series data (yielding aggregated local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The proposed Supervised Aggregative Feature Extraction (SAFE) methodology can be extended to support nonlinear predictive models by embedding the functional learning framework in a Reproducing Kernel Hilbert Spaces setting. SAFE has a number of attractive features including closed form solution and the ability to explicitly incorporate first and second order derivative information. Using simulation studies and a practical semiconductor manufacturing case study we highlight the strengths of the new methodology with respect to standard unsupervised feature extraction approaches.