Organizational Complexity in Big Science: Strategies and Practices (original) (raw)

Governing science as a complex adaptive system

Research policy is a complex matter. Copying best practices in research policy, as identified by benchmarking studies, is popular amongst policy makers but fails because of ‘knowledge asymmetries’. Research fields exhibit distinct knowledge dynamics that respond differently to governance interventions. Extending the idea of search regimes, this paper aims at providing a policy model for different knowledge dynamics by elaborating the notion of knowledge production as a complex adaptive system. Complex regimes emerge from three interacting sources of variance. In our conceptualisation, researchers are the nodes that carry the science system. Research can be considered as geographically situated practices with site specific skills, equipments and tools. The emergent science level refers to the formal communication activities of the knowledge published in journals and books, and announced in conferences. The contextual dynamics refer to the ways in which knowledge production provides resources for social and economic development. This conceptualization allows us to disaggregate knowledge dynamics both in horizontal (field related) and vertical (level related) dimensions by articulating the three different dynamics and their path dependencies (in research, science and society) in co-evolution with each other to produce distinct search regimes in each field. The implication for research governance is that generic measures can sometimes be helpful but there is clear need for disaggregated measures targeting field specific search regimes. Governing knowledge production through disaggregated measures means targeting in a distinct way not only different fields, but also, and more importantly, the interactions between local research practices, emergent scientific landscapes, and the field’s relationship to its societal context. If all three “levels” are aligned, there is a stable regime

Too big to innovate? Exploring organizational size and innovation processes in scientific research

We explore the impact of organizational size in six federally funded research organizations on a range of organizational processes related to the pursuit of innovation. The data utilized consisted of 266 scientists drawn from 64 research projects across five programmatic research areas: alternative energies, biology, chemistry, geophysical sciences, and material sciences. A sixth project category was added to accommodate the highly interdisciplinary character of a handful of projects. Although the data had some limitations, it was found that organizational size had a negative impact on three categories of innovation processes: the amount of time spent in research and professional activities, how research time is spent, and exchanges of technical knowledge. In addition, some potential advantages of larger size, such as: greater research resources, better perceived managerial quality or a visionary strategy, were not found to be significant.

Managing Science -- CHAPTER 1 TOTALITY OF SCIENCE

What is science? How is it performed? Is science only a method or is it also an institution? Here we will focus on science as a whole -- both method and institution. Science is a complicated topic because it includes both methodological and organizational aspects. Moreover, these have been historically evolving. Traditionally the philosophy and organization and history of science all have been discussed separately -- in the different literatures of philosophy of science, of sociology of science, and of history of science. However as many have recognized, this separation is one of the literature and not of practice. A consequence of this separation has been that a researcher, or a student of science, who tries to understand the real practice of science has had to read about science in three literatures -- but which even together did not necessarily integrate into a complete picture of science. This book brings these perspectives together to provide a integrated picture. For this integration, I have used the term 'totality of science' -- completeness of scientific activities. Why should one want a complete picture of science? It is important to understand science as a whole -- because science is a major force in modern society -- culturally and practically

Role of machine and organizational structure in science

PLOS ONE, 2022

The progress of science increasingly relies on machine learning (ML) and machines work alongside humans in various domains of science. This study investigates the team structure of ML-related projects and analyzes the contribution of ML to scientific knowledge production under different team structure, drawing on bibliometric analyses of 25,000 scientific publications in various disciplines. Our regression analyses suggest that (1) interdisciplinary collaboration between domain scientists and computer scientists as well as the engagement of interdisciplinary individuals who have expertise in both domain and computer sciences are common in ML-related projects; (2) the engagement of interdisciplinary individuals seem more important in achieving high impact and novel discoveries, especially when a project employs computational and domain approaches interdependently; and (3) the contribution of ML and its implication to team structure depend on the depth of ML.