Dallas with balls: televized sport, soap opera and male and female pleasures

Two of the most popular of television genres, soap opera and sports coverage have been very much differentiated along gender lines in terms of their audiences. Soap opera has been regarded very much as a 'gynocentric' genre with a large female viewing audience while the audiences for television sport have been predominantly male. Gender differentiation between the genres has had implications for the popular image of each. Soap opera has been perceived as inferior; as mere fantasy and escapism for women while television sports has been perceived as a legitimate, even edifying experience for men. In this article the authors challenge the view that soap opera and television sport are radically different and argue that they are, in fact, very similar in a number of significant ways. They suggest that both genres invoke similar structures of feeling and sensibility in their respective audiences and that television sport is a 'male soap opera'. They consider the ways in which the viewing context of each genre is related to domestic life and leisure, the ways in which the textual structure and conventions of each genre invoke emotional identification, and finally, the ways in which both genres re-affirm gender identities

Small area estimation via M-quantile geographically weighted regression

The effective use of spatial information, that is, the geographic locations of population units, in a regression model-based approach to small area estimation is an important practical issue. One approach for incorporating such spatial information in a small area regression model is via Geographically Weighted Regression (GWR). In GWR, the relationship between the outcome variable and the covariates is characterised by local rather than global parameters, where local is defined spatially. In this paper, we investigate GWR-based small area estimation under the M-quantile modelling approach. In particular, we specify an M-quantile GWR model that is a local model for the M-quantiles of the conditional distribution of the outcome variable given the covariates. This model is then used to define a bias-robust predictor of the small area characteristic of interest that also accounts for spatial association in the data. An important spin-off from applying the M-quantile GWR small area model is that it can potentially offer more efficient synthetic estimation for out of sample areas. We demonstrate the usefulness of this framework through both model-based as well as design-based simulations, with the latter based on a realistic survey data set. The paper concludes with an illustrative application that focuses on estimation of average levels of Acid Neutralising Capacity for lakes in the Northeast of the USA. <br/