Changing local elite selection in Thailand : emergence of new local government presidents after direct elections and their capabilities

Before rural local government units were established in Thailand, reform debates within the country faced a crucial issue: Candidates at the rural sub-district levels might adopt electioneering methods such as vote buying and the patronage system of the local political and economic elite, the methods that had been used in the national elections. In fact, the results of the 2006 survey in this paper, which followed the introduction of direct elections in rural local government units in 2003, contrast with the result anticipated during the debates on political reform. The preliminary data of the survey shows that the decentralization process and the introduction of the direct election system in the rural areas had some effect in changing the selection process of the local elite in Thailand.

Discriminant analysis of the specialty of elite cyclist

The different demands of competition coupled with the morphological and physiological characteristics of cyclists have led to the appearance of cycling specialities. The aims of this study were to determine the differences in the anthropometric and physiological features in road cyclists with different specialities, and to develop a multivariate model to classify these specialities and predict which speciality may be appropriate to a given cyclist. Twenty male, elite amateur cyclists were classified by their trainers as either flat terrain riders, hill climbers, or all-terrain riders. Anthropometric and cardiorespiratory studies were then undertaken. The results were analysed by MANOVA and two discriminant tests. Most differences between the speciality groups were of an anthropometric nature. The only cardiorespiratory variable that differed significantly (p < 0.05) was maximum oxygen consumption with respect to body weight (VO2max/kg). The first discriminant test classified 100% of the cyclists within their true speciality; the second, which took into account only anthropometric variables, correctly classified 75%. The first discriminant model allows the likely speciality of still non-elite cyclists to be predicted from a small number of variables, and may therefore help in their specific training.