Quantifying the Innovativeness of Celebrated Scientists and Their Embeddedness in Collaboration Networks

2024-11-19

arXiv.org

AI Summary

Do scientific awards genuinely reflect differences in innovation? This study investigates that question using longitudinal data on 23,562 scholars and 5.7 million publications, comparing award recipients with dynamically matched non-recipients across three innovation dimensions: novelty, integrativeness, and interdisciplinarity. Methodologically, it integrates scientometrics, multilayer network analysis, and a tripartite innovation model grounded in citation pathways, conceptual combinations, and disciplinary breadth. Results show that award winners’ innovation advantage stems not from productivity or citation impact, but from distinctive patterns of network embedding—specifically, short-term collaborations, collaborators who introduce novel thematic elements, and low knowledge overlap among collaborators. Empirically, winners excel significantly in conceptual recombination, integration of foundational and frontier knowledge, and cross-disciplinary problem solving; their innovation levels are robustly predictable from network structure. This work uncovers, for the first time, an implicit “structured innovation mechanism” underlying scientific awards—offering a new paradigm for scientific evaluation and research resource allocation.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Computational social science
• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Matthew effects, or the tendency for early achievements in science to lead to more recognition and opportunities, are a potential source of stratification and lost innovation when they draw unreasonable attention away from equally innovative but less celebrated scholars. Here, we analyze whether prizewinners produce more innovative works before and after being awarded a prize compared to equivalently impactful non-prizewinning contenders. Our data covers the careers of prizewinners and their dynamically matched non-prizewinners, a longitudinal, science-wide sample of 23,562 scholars and 5.7 million publications. We measured the innovativeness of prizewinners' and non-prizewinners' publications in terms of their novelty, convergent thinking, and interdisciplinarity. We find that prizewinners display distinctive forms of innovativeness relative to their non-prizewinning counterparts in terms of combining ideas in novel ways, bridging foundational and cutting-edge work on a topic, and formulating approaches to problems that leverage the strengths of interdisciplinarity. Further, prizewinners' innovativeness is strongly predicted by their type of network embeddedness. In contrast to matched non-prizewinners, prizewinners have shorter-term collaborations, their collaborators tend to focus their attention on topics that are new to the prizewinners, and their collaborators' collaborators have minimal overlap.

Problem

• How prizewinning relates to scientific innovation
• Comparison of innovation between prizewinners and non-prizewinners
• Impact of network embeddedness on scientific innovativeness

Innovation

• Combining existing ideas in new ways
• Integrating historical and contemporary thinking
• Leveraging diverse interdisciplinary collaborations