Alltagswissen – Körperwissen – Praxiswissen – Fachwissen. Zur Aneignung,Bewertungs‐ und Orientierungslogik von Wissenskulturen

Everyday knowledge – body knowledge – knowledge of experience – specialized knowledge: Acquisition, assessment and the orientation of logic concerning cultures of knowledge. – The essay explores changes in the understanding, legitimisation, and practice of midwifery. It was one of the earliest professional activities for women. During the eighteenth century a new culture of expertise emphasized theoretical knowledge and adherence to medical disciplines over the empirical practice gained by women. This early phase of professionalisation, with its hierarchies and preferred use of medically accredited knowledge, was not, however, solely divided along gender lines. Female professionalism was not just supplanted by male academic medicalisation. New ways of attaining and assessing knowledge, a different perception of how it is organised, and above all, social change created new patterns of understanding. This process achieved a new professional ethos. In pursuing the issue of gender, various examples are chosen to illustrate how changes in scientific knowledge and its relevant application are mediated. The construct of scientific knowledge and how it is used reflects gender relations and power structures. There is not only competition between female and male perceptions of knowledge, but also male stereotyping of female knowledge, in particular male notions of what kind of knowledge is necessary and how this is perceived by women. Karen Offen used the term ‘knowledge wars’ to describe how a monopoly of scientific expertise and relevant knowledge works within the professions.     

A Hybrid Framework for Space–Time Modeling of Environmental Data. 环境数据时空建模的混合框架

The space–time autoregressive integrated moving average (STARIMA) model family provides useful tools for modeling space–time processes that exhibit stationarity (or near stationarity) in space and time. However, a more general method for routine use and efficient computation is needed to model the nonlinearities and nonstationarities of environmental space–time series. This article presents a hybrid framework combining machine learning and statistical methods to address this issue. It uses an artificial neural network (ANN) to extract global deterministic (nonlinear) space–time trends and a STARIMA model to extract local stochastic space–time variations in data. A four‐stage procedure is proposed for analyzing and modeling space–time series. The proposed framework and procedures are applied to forecast annual average temperature at 137 national meteorological stations in China. The results demonstrate that the hybrid framework achieves better forecasting accuracy than the STARIMA model alone. This finding suggests that the combination of machine learning and statistical methods provides a very powerful tool for analyzing and modeling space–time series of environmental data that have strong spatial nonlinear and nonstationary components.