Modelling science - dynamic models of knowledge production

Mid term and long term collaboration.

Modelling science - dynamic models of knowledge production

Models of science can take very different forms from conceptual models based on historical and ethnographic observations to mathematical descriptions of measurable phenomena. In these models, scholars and science itself become “research objects”. The philosophy, history and sociology of science have produced valuable insights in the nature of scholarly activities as a human activity and social system. Within this area, the dynamics and structure of the system of the sciences, including the social sciences and humanities, have been the focus of a variety of explanatory, exploratory, and metaphorical models (Kuhn 1962, Cole and Cole 1967, Crane 1972, Elkana 1978, Nowakowska 1984; Price 1963, Nalimov, Mulchenko 1969, Leydesdorff, van den Besselaar 1997).

After World War II, scientists were increasingly the subject of systematic and large scale measurements efforts. The growth and changing roles of science stimulated the need for governmental and “policy support of science” as well as the need for an empirical basis for “science policy”. A host of monitoring and evaluative indicators were created. Sociology of science (Bernal 1939, Kuhn 1962, Merton 1973) as well as Scientometrics (Nalimov, Mulchenko 1969; Price 1963) were established as scientific fields. The Society for Social Studies of Science (4S), the European Associations for the Study of Science and Technology (EASST) and the International Society of Scientometrics and Informetrics (ISSI), among others, are active as professional organisations in this field. At their conferences “models of science” appear occasionally, but are not presented in s systematic way on a regular basis. Also, other knowledge domains, such as sociology, philosophy, economics, but also physics apply their models to science.

Models of science, and here I specifically refer to mathematical models, have a rich history. Almost every progress in mathematical modelling has also been applied to model science itself. Phenomena such as specific growth laws of publications and citations (Price 1965, 1976), scientific productivity (Lotka 1926), or the distribution of topics over journals (Bradford 1934) have always raised the interest of mathematicians and natural scientists. Mathematical models have been proposed to explain statistically regularities (Egghe, Rousseau 1990) but also to model the spreading of ideas (Goffman 1966), the competition between scientific paradigms (Sterman 1985) and fields (Kochen 1983; Yablonsky 1986, Bruckner et al. 1990), to model the relation between publishing, referencing, and the emergence of new topics (Gilbert 1997), and the co-evolution of co-author and paper-citation networks (Börner et al 2004).

This long-term collaborative projects aims to create a reference point for the scattered models of science. In this project we organize activities to support an exchange of knowledge between different disciplinary and methodological approaches as well as to foster the contextualization of mathematical models in science and technology studies.

30/01/2010
Locations: Amsterdam

Activities for project

07/10/2009
08/10/2009
15/02/2010 - 17/02/2010
23/09/2010 - 24/09/2010
09/10/2010 - 10/10/2010