Towards a new crown indicator: Some theoretical considerations

The crown indicator is a well-known bibliometric indicator of research performance developed by our institute. The indicator aims to normalize citation counts for differences among fields. We critically examine the theoretical basis of the normalization mechanism applied in the crown indicator. We also make a comparison with an alternative normalization mechanism. The alternative mechanism turns out to have more satisfactory properties than the mechanism applied in the crown indicator. In particular, the alternative mechanism has a so-called consistency property. The mechanism applied in the crown indicator lacks this important property. As a consequence of our findings, we are currently moving towards a new crown indicator, which relies on the alternative normalization mechanism

Evaluating a Department’s Research: Testing the Leiden Methodology in Business and Management

The Leiden methodology (LM), also sometimes called the “crown indicator”, is a quantitative method for evaluating the research quality of a research group or academic department based on the citations received by the group in comparison to averages for the field. There have been a number of applications but these have mainly been in the hard sciences where the data on citations, provided by the ISI Web of Science (WoS), is more reliable. In the social sciences, including business and management, many journals and books are not included within WoS and so the LM has not been tested here. In this research study the LM has been applied on a dataset of over 3000 research publications from three UK business schools. The results show that the LM does indeed discriminate between the schools, and has a degree of concordance with other forms of evaluation, but that there are significant limitations and problems within this discipline

Simulating the Social Processes of Science

Science is the result of a substantially social process. That is, science relies on many inter-personal processes, including: selection and communication of research findings, discussion of method, checking and judgement of others' research, development of norms of scientific behaviour, organisation of the application of specialist skills/tools, and the organisation of each field (e.g. allocation of funding). An isolated individual, however clever and well resourced, would not produce science as we know it today. Furthermore, science is full of the social phenomena that are observed elsewhere: fashions, concern with status and reputation, group-identification, collective judgements, social norms, competitive and defensive actions, to name a few. Science is centrally important to most societies in the world, not only in technical, military and economic ways, but also in the cultural impacts it has, providing ways of thinking about ourselves, our society and our environment. If we believe the following: simulation is a useful tool for understanding social phenomena, science is substantially a social phenomenon, and it is important to understand how science operates, then it follows that we should be attempting to build simulation models of the social aspects of science. This Special Section of <i>JASSS</i> presents a collection of position papers by philosophers, sociologists and others describing the features and issues the authors would like to see in social simulations of the many processes and aspects that we lump together as "science". It is intended that this collection will inform and motivate substantial simulation work as described in the last section of this introduction.Simulation, Science, Science and Technology Studies, Philosophy, Sociology, Social Processes