Dynamic Patterns in Processes of Science Systems: Science Academies & their Journals-An illustrative example

Submitted by sys1 on Mon, 01/23/2017 - 15:28
Journal of Scientometic Research,2016,5,3,189-196.
Published:December 2016
Type:Research Article

Dynamic Patterns in Processes of Science Systems: Science Academies & their Journals-An illustrative example

Ashok Jain1 and Mrinal Kanti Das2

1EMPI Business School, New Delhi, INDIA.

2Institute of Informatics & Communication, University of Delhi South Campus, Benito-Juarez Road, New Delhi-110021, INDIA.

Abstract:

The paper presents a quantitative method of comparing the hitherto less explored processes embedded in science activities performed by different organisations. The method requires only time series data of the output of activity. As an illustration, the method is applied to compare the processes embedded in activity of academies in the US, Japan, China (Taiwan) and India; the chosen activity is meant to disseminate results of scientific results through English language journals. The activity is viewed as an activity performed by a complex system involving interactions at different times between paper contributors, finance providers, peers, readers, organisations and structures responsible for its publication and distribution. The paper estimates Permutation Entropy as a complexity index to characterise processes embedded in the activity. The method circumvents the need of measurable data on individual actors and agencies involved in the activity. Results reveal similarities in processes adopted by academies in US and Japan and hence they constitute a cluster; academies of Taiwan and India lay out side this cluster. Referring to literature on organisational learning, it is pointed out that complexity index also reflects the ability of the system to learn from experience, an observation that has policy implications. Finally it is noted that as the method requires only time series data of the activity out put, it can be applied for inter country comparisons of processes and learning abilities embedded in other science and technology sub systems of National Innovation System and can in principle be used as an additional tool for cross-country comparisons of National Innovation Systems.

Motifs or ordinal pattern count for S, D, T and M time series in different journals.