手法を比較

	Disruption Index (CD-Index)	Usage Bibliometrics (Downloads and COUNTER)
分野	計量書誌学	計量書誌学
系統	Process / pipeline	Process / pipeline
提唱年	2017	2009
提唱者	Russell J. Funk & Jason Owen-Smith; Lingfei Wu, Dashun Wang & James A. Evans	Johan Bollen, Herbert Van de Sompel & colleagues (MESUR project)
種類	Citation-network pipeline for classifying contributions as disruptive or consolidating	Usage-log pipeline for impact metrics from downloads and views
原典	Funk, R. J., & Owen-Smith, J. (2017). A Dynamic Network Measure of Technological Change. Management Science, 63(3), 791-817.	Bollen, J., Van de Sompel, H., Hagberg, A., & Chute, R. (2009). A Principal Component Analysis of 39 Scientific Impact Measures. PLoS ONE, 4(6), e6022.
別名	CD Index, Consolidation-Disruption Index, CD5 Index, Disruptiveness Measure	Download Metrics, Usage Factor Analysis, Usage-Based Impact Metrics, COUNTER Usage Analysis
関連	3	3
概要	The disruption index, or CD index, classifies a scientific paper or patent by how the work that cites it treats the work it built on. Introduced by Russell Funk and Jason Owen-Smith in 2017 as a dynamic network measure of technological change, and popularized for science by Lingfei Wu, Dashun Wang, and James Evans in 2019, it asks a simple structural question: when later researchers cite a focal work, do they also keep citing that work's own references, or do they cite the focal work instead of its predecessors? If subsequent work cites the focal item but largely ignores its references, the item has disrupted its field, eclipsing what came before; if subsequent work cites both the item and its references together, the item has consolidated existing knowledge. The index runs from -1 (purely consolidating) to +1 (purely disrupting) and has become a standard tool for measuring whether contributions push science in new directions or deepen established lines.	Usage bibliometrics measures the impact of scholarly works from how often they are downloaded and viewed rather than how often they are cited. Drawing on server and publisher logs standardized through the COUNTER code of practice, it turns raw access events into impact indicators such as the usage factor. The MESUR project led by Johan Bollen and Herbert Van de Sompel was pivotal: their 2008 work demonstrated usage-based impact metrics built from large-scale usage logs, and their 2009 principal component analysis of thirty-nine impact measures showed that scientific impact is multidimensional, with usage metrics occupying a distinct region of the space from citation metrics. Usage signals accrue almost immediately and reflect a far larger readership than the subset of authors who eventually cite, making them an early and broad complement to citation analysis, provided the logs are carefully standardized.