The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index; none of the 7 sources its claims cite is checked for support yet.
Universality of citation distributions: Toward an objective measure of scientific impact
Open accessAbstract
We study the distributions of citations received by a single publication within several disciplines, spanning broad areas of science. We show that the probability that an article is cited c times has large variations between different disciplines, but all distributions are rescaled on a universal curve when the relative indicator c f = c / c 0 is considered, where c 0 is the average number of citations per article for the discipline. In addition we show that the same universal behavior occurs when citation distributions of articles published in the same field, but in different years, are compared. These findings provide a strong validation of c f as an unbiased indicator for citation performance across disciplines and years. Based on this indicator, we introduce a generalization of the h index suitable for comparing scientists working in different fields.
Overview
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TL;DR
The paper shows that citation distributions across scientific disciplines and publication years collapse onto a universal curve when citations are rescaled by the field-year average \(c_0\), making \(c_f=c/c_0\) an unbiased relative indicator. It introduces a generalized h-index \(h_f\) based on \(c_f\) and normalized publication rates.
Key findings
- Bare citation distributions vary strongly by discipline: a publication with 100 citations is approximately 50 times more common in Developmental Biology than in Engineering, Aerospace. After rescaling by \(c_0\), all distributions collapse onto a universal curve fitted by a lognormal with \(\sigma^2=1.3\).
- Universality also holds across years within the same field: for Hematology, Neuroimaging, and Physics, Nuclear, articles from 1990, 1999, and 2004 show the same rescaled distribution despite \(c_0\) growing with elapsed time.
- Ranking articles by \(c_f\) reduces field bias: for the top \(z=20\%\), the standard deviation of discipline bin heights is \(\sigma_z=1.15\%\) for \(c_f\)-based ranking versus \(12.37\%\) for bare \(c\), compared with the theoretical unbiased value \(\sigma_z=1.09\%\).
- The generalized h-index \(h_f\) orders articles by \(c_f\) and uses reduced rank \(r/N_0\); an example with \(c_f\) values 4.1, 2.8, 2.2, 1.6, 0.8, 0.4 and \(N_0=2.0\) gives \(h_f=1.5\). Author publication counts also rescale, fitted by a power law with exponent \(-3.5(5)\).
Methods
- Data come from Thomson Scientific’s Web of Science; journals are divided into 172 JCR categories, and only records classified as “article” and “letter” are considered. Citations are counted as the total number of times an article appears as a reference in a more recent published article.
- For each discipline and year, the average citations per article \(c_0\) is computed; distributions \(P(c,c_0)\) are rescaled as \(c_0P(c,c_0)\) versus \(c_f=c/c_0\). Curves are fitted with a lognormal constrained by \(\sigma^2=-2\mu\) because the expected value of \(c_f\) is 1, using exponentially growing bins.
- Ranking analysis pools disciplines, ranks articles by \(c\) and by \(c_f\), and compares the percentage of each discipline in the top \(z\%\) against an unbiased-ranking expectation. The generalized h-index is defined using \(c_f\) and \(r/N_0\).
Limitations
- Stated: uncited articles are neglected, though the authors verified their inclusion only produces a small shift in \(c_0\); “Multidisciplinary sciences” does not fit perfectly because its citation distribution is a convolution of single disciplines, but restricting to Nature, Science, and PNAS fits well.
- Stated: the analysis addresses single publications, not individuals or research groups, and other biases such as number of authors are left for future work; the origin of the universality is also not understood.
- Evident from the text: JCR categories are not mutually exclusive, and the generalized h-index’s practical application is deferred to a forthcoming publication.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example a publication with 100 citations is approximately 50 times more common in 'Developmental Biology' than in 'Engineering, Aerospace'.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Fitting a single curve for all categories, a lognormal distribution with σ 2 = 1 . 3 is found, that is reported in Figure 2.”
- Also stated in: § II. VARIABILITY OF CITATION STATISTICS IN DIFFERENT DISCIPLINES · p. 2 · plain
“The dashed line is a lognormal fit with σ 2 = 1 . 3.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § II. VARIABILITY OF CITATION STATISTICS IN DIFFERENT DISCIPLINES · p. 2 · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Values of χ 2 per degree of freedom, also reported in Table I, indicate that the fit is good.”
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition we show that the same universal behavior occurs when citation distributions of articles published in the same field, but in different years, are compared.”
- Also stated in: § III. DISTRIBUTION OF THE RELATIVE INDICATOR c f · p. 4 · plain
“The value of c 0 obviously grows as older publications are considered, but the rescaled distribution remains conspicuously the same.”
- Also stated in: § IV. A GENERALIZED H-INDEX · p. 5 · plain
“the universal scaling observed over different disciplines naturally holds also for articles published in different periods of time.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § III. DISTRIBUTION OF THE RELATIVE INDICATOR c f · p. 4 · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As Fig. 5 shows, such a quantity also depends on the discipline considered: in some disciplines the average number of articles published by an author in a year is much larger than in others.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Interestingly, the universal curve is fitted reasonably well over almost two decades by a power-law behavior P ( N,N 0 ) ≈ ( N/N 0 ) -δ with δ = 3 . 5(5).”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“In agreement with the findings of Ref. [11] c 0 shows very little correlation with the overall size of the field, as measured by the total number of articles.”
What the sources say
- Not checked yetcites [11] Althouse BM, West JD, Bergstrom T, Bergstrom CT (2008) Differences in Impact Factor Across Fields and Over Time. arxiv 0804.3116 . source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The category 'Multidisciplinary sciences' does not fit perfectly into the universal picture found for other categories, because the distribution of the number of citations is a convolution of the distributions corresponding to the single disciplines represented in the journals.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, if one restricts only to the three most important multidisciplinary journals ( Nature , Science , Proc. Natl. Acad. Sci. USA ) also this category fits very well into the global universal picture.”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“all distributions are rescaled on a universal curve when the relative indicator c f = c/c 0 is considered, where c 0 is the average number of citations per article for the discipline.”
- Also stated in: § III. DISTRIBUTION OF THE RELATIVE INDICATOR c f · p. 2 · boosted
“Fig. 2 shows that this procedure leads to a very good collapse of all curves for different values of c 0 onto a single shape.”
- Also stated in: § III. DISTRIBUTION OF THE RELATIVE INDICATOR c f · p. 2 · boosted
“This allows to conclude that, rescaling the distribution of citations for publications in a scientific discipline by their average number, a universal curve is found, independent of the specific discipline.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § III. DISTRIBUTION OF THE RELATIVE INDICATOR c f · p. 2 · boosted
- It may state two separate claims as one. 5
- Probably not a substantial claim. 1
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, for z = 20%, σ z = 1 . 15% for c f -based ranking, while σ z = 12 . 37% if c is used, as opposed to the value σ z = 1 . 09% in the hypothesis of unbiased ranking.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: Under an unbiased ranking, articles of the various disciplines are scattered uniformly along the rank axis.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“All fitted values of σ 2 , reported in Table I, are compatible within two standard deviations, except for one (Anesthesiology) that is in any case within three standard deviations of all the others.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When the ranking is performed according to c f = c/c 0 we find (Table II) a very good agreement with the hypothesis that the ranking is unbiased, while strong evidence that the ranking is biased is found when c is used.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: Under an unbiased ranking, articles of the various disciplines are scattered uniformly along the rank axis.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The value of c 0 obviously grows as older publications are considered, but the rescaled distribution remains conspicuously the same.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“It is known that different temporal patterns of citations exist, with some articles starting soon to receive citations, while others ('sleeping beauties') go unnoticed for a long time, after which they are recognized as seminal and begin to attract a large number of citations [26, 27].”
What the sources say
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
The authors state this as an assumption.
Under an unbiased ranking, articles of the various disciplines are scattered uniformly along the rank axis.
“assuming that articles of the various disciplines are scattered uniformly along the rank axis, one would expect the average bin height in Fig. 3 to be z % with a standard deviation”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The number of citations a paper receives is the most commonly used measure of scienti fi c impact.”
From Unraveling scientific impact: Citation types in marketing journals (2015) · cites this paper as Radicchi, Fortunato, & Castellano, 2008
In the citing paper
“Variability in citation practices across disciplines, for instance, is a well-recognized problem which prevents direct comparability of citation counts across fi elds (Bornmann & Daniel, 2008), unless appropriate corrective methods are applied (Radicchi et al., 2008).”
From Unraveling scientific impact: Citation types in marketing journals (2015) · cites this paper as Radicchi et al., 2008
In the citing paper
“Recent work has improved our theoretical understanding of citation dynamics [27–29].”
From Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level (2016) · cites this paper as [28]
Citations
Cites
An index to quantify an individual’s scientific research output
Hirsch, J. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0507655102 · Cited by 11,669
Theory and practise of the g-index
Egghe, L. (2006) · Scientometrics · doi:10.1007/s11192-006-0144-7 · Cited by 2,354
Citation Indexing-Its Theory and Application in Science, Technology, and Humanities
Goodwin, J., & Garfield, E. (1980) · Technology and Culture · doi:10.2307/3104125 · Cited by 1,648
What do citation counts measure? A review of studies on citing behavior
Bornmann, L., & Daniel, H. (2008) · Journal of Documentation · doi:10.1108/00220410810844150 · Cited by 1,607
How popular is your paper? An empirical study of the citation distribution
Redner, S. (1998) · The European Physical Journal B · doi:10.1007/s100510050359 · Cited by 1,509
The scientific impact of nations
King, D. A. (2004) · Nature · doi:10.1038/430311a · Cited by 1,245
Does the h index have predictive power?
Hirsch, J. E. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0707962104 · Cited by 980
van Raan, A. F. J. (2004) · Scientometrics · doi:10.1023/b:scie.0000018543.82441.f1 · Cited by 579
Moed, H. F., Bruin, R. E. D., & van Leeuwen, T. N. (1995) · Scientometrics · doi:10.1007/bf02017338 · Cited by 502
Schubert, A. P., & Braun, T. (1986) · Scientometrics · doi:10.1007/bf02017249 · Cited by 450
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Stuart, A., Faucette, S. P., & Thomas, W. J. (2017) · Journal of Speech Language and Hearing Research · doi:10.1044/2017_jslhr-h-16-0458 · Cited by 14
Liu, M., Gao, Y., Yuan, Y., Shi, S., Yang, K., Wu, J., Zhang, J., & Tian, J. (2020) · Future Oncology · doi:10.2217/fon-2019-0810 · Cited by 14
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Gu, J. (2022) · Applied Environmental Biotechnology · doi:10.26789/aeb.2022.02.001 · Cited by 14
Percentile-Based Journal Impact Factors: A Neglected Collection Development Metric.
Wagner, A. B. (2009) · Issues in Science and Technology Librarianship · doi:10.29173/istl2476 · Cited by 14
Sangwal, K. (2012) · Journal of Informetrics · doi:10.1016/j.joi.2012.09.002 · Cited by 14
Denrell, J. C., Liu, C., & Mens, G. L. (2017) · Strategy Science · doi:10.1287/stsc.2017.0025 · Cited by 14
Vȋiu, G.-A. (2018) · Journal of Informetrics · doi:10.1016/j.joi.2018.02.002 · Cited by 14
Nature, Science, and PNAS: disciplinary profiles and impact
Milojević, S. (2020) · Scientometrics · doi:10.1007/s11192-020-03441-5 · Cited by 14
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Lü, H., Zhu, Y., Lin, Q., Wang, T., Niu, Z., & Herrera‐Viedma, E. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3041904 · Cited by 14
The advantages of interdisciplinarity in modern sciencePreprint
Bonaventura, M., Latora, V., Nicosia, V., & Panzarasa, P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.07910 · Cited by 14
Rank analysis of most cited publications, a new approach for research assessments
Rodríguez‐Navarro, A., & Brito, R. (2024) · Journal of Informetrics · doi:10.1016/j.joi.2024.101503 · Cited by 13
Ruiz‐Castillo, J. (2013) · Journal of Informetrics · doi:10.1016/j.joi.2013.10.002 · Cited by 13
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Wang, X., & Zhang, Z. (2020) · Journal of Informetrics · doi:10.1016/j.joi.2020.101019 · Cited by 13
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Axelsson, R., Ljung, M., Blicharska, M., Frisk, M., Henningsson, M., Mikusiński, G., Folkeson, L., Göransson, G., Jönsson-Ekström, S., Sjölund, A., et al. (2020) · Sustainability · doi:10.3390/su12176995 · Cited by 14
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You, Z.-Q., Han, X.-P., & Hadzibeganovic, T. (2015) · Physics Letters A · doi:10.1016/j.physleta.2015.12.022 · Cited by 12
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Klosik, D. F., Bornholdt, S., & Hütt, M. ‐. (2014) · Physical Review E · doi:10.1103/physreve.90.032811 · Cited by 8
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Metadata
- Authors
- Radicchi, F., Fortunato, S., & Castellano, C.
- Affiliations
- Institute for Scientific Interchange (IT)
- National Research Council (IT)
- Corresponding author
- Castellano, Claudio
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2008-11-11
- Volume
- 105
- Issue
- 45
- Pages
- 17268-17272
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC2582263
- OpenAlex
- W2151866568
- Semantic Scholar
- 1ead99845d1dc1c0d58e47eda9ba21aa0234878f
- Cited by
- 828
- References
- 31
- Citations by year
- 2026: 20 · 2025: 38 · 2024: 42 · 2023: 40 · 2022: 41 · 2021: 48 · 2020: 53 · 2019: 64 · 2018: 44 · 2017: 75 · 2016: 58 · 2015: 56 · 2014: 57 · 2013: 61 · 2012: 48
- FWCI
- 31.18
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Library and Information Sciences › scientometrics and bibliometrics research
- Physical Sciences › Computer Science › Information Systems › Web visibility and informetrics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychology Research and Bibliometrics
- Keywords
- citation distribution, scientific impact, h-index, cross-disciplinary comparison, rescaling, citation performance, bibliometric analysis
- MeSH terms
- Models, Theoretical, Selection Bias, Interdisciplinary Communication, Journal Impact Factor
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-06 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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