Dynamic patterns of the disruptive and consolidating knowledge flows in Nobel-winning scientific breakthroughs
Bibliographic Data
| ID | 21255950 |
|---|---|
| Authors | Alex J Yang (0000-0002-1276-0483, Nanjing University of Information Science and Technology), Sanhong Deng (0000-0002-6910-3935, Nanjing University of Information Science and Technology) |
| Year | 2024 |
| Volume | 5 |
| Issue | 4 |
| Pages | 1070-1086 |
| Publication date | 2024-11-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Quantitative Science Studies (JOURNAL) |
| Journal identifiers | ISSN: 2641-3337 • E-ISSN: 2641-3337 |
| Publisher | MIT Press (PUBLISHER • US) |
| DOI | 10.1162/qss_a_00323 |
| OpenAlex | W4401358970 |
| Language | EN |
| Citations received | 4 |
| References cited | 65 |
Scientific breakthroughs possess the transformative potential to reshape research trajectories and scientific paradigms. However, there is limited systematic evidence on how these breakthroughs influence the evolution of scientific knowledge. Building on the concepts of disruption and consolidation in science, we categorize forward-citing papers into two distinct categories: Disruptive Citing Papers (DCP) and Consolidating Citing Papers (CCP). Analyzing the dynamic patterns of DCP and CCP in Nobel Prize–winning papers, we find that in the early postpublication phase, scientific breakthroughs generate more consolidating citations than disruptive citations. Additionally, CCP in this early phase demonstrate higher scientific impact. However, in the long-term phase, scientific breakthroughs generate more disruptive citations, with DCP often involving larger and more diverse teams. Linguistic analysis also uncovers nuanced differences between CCP and DCP. Furthermore, the dynamic patterns of knowledge flow in scientific breakthroughs differ significantly from control groups. Collectively, our results reveal that scientific breakthroughs initially consolidate knowledge before disrupting it in later phases, offering profound insights into the mechanisms driving scientific progress
Biology · Business · Categorization · Data science · Engineering ethics · Scientific literature · Social science · Sociology · Sociology of scientific knowledge · Transformative learning · Complex Network Analysis Techniques · Complex Systems and Time Series Analysis · Computer Science · Engineering · Psychology · scientometrics and bibliometrics research · Artificial Intelligence
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Gender-diverse teams produce more novel and higher-impact scientific ideas
The structure of invention
Large teams develop and small teams disrupt science and technology
Multi-University Research Teams
Science of science
Atypical Combinations and Scientific Impact
Papers and patents are becoming less disruptive over time
Bibliographic coupling between scientific papers
The science of science
Quantifying the evolution of individual scientific impact
The Increasing Dominance of Teams in Production of Knowledge
Rescaling the disruption index reveals the universality of disruption distributions in science
Microsoft Academic Graph
Matthew effects in science and the serial diffusion of ideas
See further upon the giants
Against method
A typology of scientific breakthroughs
A multidimensional framework for characterizing the citation impact of scientific publications
A meta-analysis of semantic classification of citations
Nobel Laureates in Science
Data, measurement and empirical methods in the science of science
| Citations per year | 2 |
|---|---|
| Citation span | 2024 - 2026 (3) |
| Highly cited | No |