Professional football in apartheid South Africa:leisure, consumption and identity in the national football league, 1959-1977

A growing body of academic and popular literature considers the history of South African football. These and existing publications pay little or no attention to the emergence of white professional football in apartheid South Africa. The National Football League (NFL) challenged the amateur game and introduced professional football to the country. During its 17-year existence, the NFL grew each season with large attendances until its demise in 1977. In addition, the NFL imported a range of international players, invited foreign teams and actively engaged in the political debates in South African sport at the time. The NFL was instrumental in popularising the game across the country for all South Africans. The NFL became the most popular sports entertainment of choice for South Africans during this period. Finally, the NFL actively engaged in a campaign of destroying rival non-racial anti-apartheid leagues while simultaneously co-opting less progressive organisations. © 2013 Taylor and Francis.

Ontology forecasting in scientific literature:semantic concepts prediction based on innovation-adoption priors

The ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t + 1, using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years.