Leisure, Popular Culture and Memory: The Invention of Dark Age Britain, Wales, England, and Middle-earth in the songs of Led Zeppelin

In the period of high modernity, and in the process of establishing the imperial nation-state of Great Britain, historians, archaeologists and enthusiastic amateurs searched high and low for material evidence and primary sources from what was called the Dark Ages. There is a gap in knowledge about this past, and all discussion rests on finding meaning in fading inscriptions, or dark earth, or trusting completely the writings of Bede and Gildas. The search for an identity and history for the nation for Great Britain was based on nationalist beliefs about Englishness, Britishness or Welshness. In the twentieth-century, the problem of Englishness, place and myth led Tolkien to write his Middle-earth stories in his leisure time. At the same time, the problem of Welshness or Britishness saw a growth in interest – in film and books - in Arthurian traditions, and a tourist interest in the Celtic fringe of Britain. In this paper, I show how the songs and album covers of Led Zeppelin, and their film The Song Remains the Same, draw upon both the work of Tolkien and the Arthurian traditions to construct ideas of masculine belonging in some mythological medieval time and place. While this constriction is idiosyncratic to the artists, they are drawing on and justifying the wider problem of England, Wales and Britain in leisure and culture

Mapping the Paediatric Quality of Life Inventory (PedsQL™) Generic Core Scales onto the Child Health Utility Index–9 Dimension (CHU-9D) Score for Economic Evaluation in Children

Background: The Paediatric Quality of Life Inventory (PedsQL™) questionnaire is a widely used, generic instrument designed for measuring health-related quality of life (HRQoL); however, it is not preference-based and therefore not suitable for cost–utility analysis. The Child Health Utility Index–9 Dimension (CHU-9D), however, is a preference-based instrument that has been primarily developed to support cost–utility analysis. Objective: This paper presents a method for estimating CHU-9D index scores from responses to the PedsQL™ using data from a randomised controlled trial of prednisolone therapy for treatment of childhood corticosteroid-sensitive nephrotic syndrome. Methods: HRQoL data were collected from children at randomisation, week 16, and months 12, 18, 24, 36 and 48. Observations on children aged 5 years and older were pooled across all data collection timepoints and were then randomised into an estimation (n = 279) and validation (n = 284) sample. A number of models were developed using the estimation data before internal validation. The best model was chosen using multi-stage selection criteria. Results: Most of the models developed accurately predicted the CHU-9D mean index score. The best performing model was a generalised linear model (mean absolute error = 0.0408; mean square error = 0.0035). The proportion of index scores deviating from the observed scores by 13 years) or patient groups with particularly poor quality of life. ISRCTN Registry No: 1664524

Building an Ensemble for Software Defect Prediction Based on Diversity Selection

Background: Ensemble techniques have gained attention in various scientific fields. Defect prediction researchers have investigated many state-of-the-art ensemble models and concluded that in many cases these outperform standard single classifier techniques. Almost all previous work using ensemble techniques in defect prediction rely on the majority voting scheme for combining prediction outputs, and on the implicit diversity among single classifiers. Aim: Investigate whether defect prediction can be improved using an explicit diversity technique with stacking ensemble, given the fact that different classifiers identify different sets of defects. Method: We used classifiers from four different families and the weighted accuracy diversity (WAD) technique to exploit diversity amongst classifiers. To combine individual predictions, we used the stacking ensemble technique. We used state-of-the-art knowledge in software defect prediction to build our ensemble models, and tested their prediction abilities against 8 publicly available data sets. Conclusion: The results show performance improvement using stacking ensembles compared to other defect prediction models. Diversity amongst classifiers used for building ensembles is essential to achieving these performance improvements