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	Disruption Index (CD-Index)	Citation Distribution Modeling (Lognormal/Tsallis)
Campo	Bibliometria	Bibliometria
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	2017	2008
Ideatore	Russell J. Funk & Jason Owen-Smith; Lingfei Wu, Dashun Wang & James A. Evans	Filippo Radicchi, Santo Fortunato & Claudio Castellano
Tipo	Citation-network pipeline for classifying contributions as disruptive or consolidating	Statistical-modeling pipeline for the shape of citation distributions
Fonte seminale	Funk, R. J., & Owen-Smith, J. (2017). A Dynamic Network Measure of Technological Change. Management Science, 63(3), 791-817.	Radicchi, F., Fortunato, S., & Castellano, C. (2008). Universality of citation distributions: Toward an objective measure of scientific impact. Proceedings of the National Academy of Sciences, 105(45), 17268-17272.
Alias	CD Index, Consolidation-Disruption Index, CD5 Index, Disruptiveness Measure	Citation Distribution Analysis, Universality of Citation Distributions, Relative Citation Indicator, Discounted Cumulative Citation Modeling
Correlati	3	3
Sintesi	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.	Citation distribution modeling studies the statistical shape of how citations are spread across papers and uses that shape to compare impact fairly across very different fields. The pivotal result, from Filippo Radicchi, Santo Fortunato, and Claudio Castellano in 2008, is that although raw citation distributions differ enormously between disciplines, they collapse onto a single universal curve once each paper's citations are divided by the average for its field and year. This relative indicator turns an unfair comparison, a mathematics paper against a biomedicine paper, into a fair one by asking how each paper performs relative to its own field's baseline. The universal curve is well described by a lognormal form, and related work has used Tsallis or stretched-exponential and discounted-cumulative formulations, giving scientometrics a principled statistical foundation for normalization rather than ad hoc field adjustments.