Yöntem Karşılaştırma

	Usage Bibliometrics (Downloads and COUNTER)	Citation Distribution Modeling (Lognormal/Tsallis)
Alan	Bibliyometri	Bibliyometri
Aile	Process / pipeline	Process / pipeline
Köken yılı	2009	2008
Köken	Johan Bollen, Herbert Van de Sompel & colleagues (MESUR project)	Filippo Radicchi, Santo Fortunato & Claudio Castellano
Tür	Usage-log pipeline for impact metrics from downloads and views	Statistical-modeling pipeline for the shape of citation distributions
Seminal kaynak	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.	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.
Diğer adlar	Download Metrics, Usage Factor Analysis, Usage-Based Impact Metrics, COUNTER Usage Analysis	Citation Distribution Analysis, Universality of Citation Distributions, Relative Citation Indicator, Discounted Cumulative Citation Modeling
İlişkili	3	3
Özet	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.	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.