Role of machine and organizational structure in science

Abstract

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.

Big data, machine learning and uncertainty in foresight studies

“…Such blended competencies also facilitate the communication between stakeholders, enhancing participation, improving futures literacy and ensuring the quality of processes and results in foresight (Keller and von der Gracht, 2014). To this end, data scientists can contribute significantly by handling data collection, storage and cleaning, while providing accurate interpretations of analyses, in collaboration with practitioners and domain experts (Pankratova and Savastiyanov, 2014;Thu et al, 2022).…”