Comment on "Quantifying long-term scientific impact"

Jian Wang; Yajun Mei; Diana Hicks

doi:10.1126/science.1248770

Comment on "Quantifying long-term scientific impact"

Jian Wang, Yajun Mei, Diana Hicks

Global Public Health

Research output: Contribution to journal › Comment/debate › peer-review

Abstract

Wang et al. (Reports, 4 October 2013, p. 127) claimed high prediction power for their model of citation dynamics. We replicate their analysis but find discouraging results: 14.75% papers are estimated with unreasonably large μ (>5) and λ (>10) and correspondingly enormous prediction errors. The prediction power is even worse than simply using short-term citations to approximate long-term citations.

Original language	English (US)
Pages (from-to)	149b
Journal	Science
Volume	345
Issue number	6193
DOIs	https://doi.org/10.1126/science.1248770
State	Published - 2014

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title = "Comment on {"}Quantifying long-term scientific impact{"}",

abstract = "Wang et al. (Reports, 4 October 2013, p. 127) claimed high prediction power for their model of citation dynamics. We replicate their analysis but find discouraging results: 14.75% papers are estimated with unreasonably large μ (>5) and λ (>10) and correspondingly enormous prediction errors. The prediction power is even worse than simply using short-term citations to approximate long-term citations.",

author = "Jian Wang and Yajun Mei and Diana Hicks",

year = "2014",

doi = "10.1126/science.1248770",

language = "English (US)",

volume = "345",

pages = "149b",

journal = "Science",

issn = "0036-8075",

publisher = "American Association for the Advancement of Science",

number = "6193",

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T1 - Comment on "Quantifying long-term scientific impact"

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AU - Mei, Yajun

AU - Hicks, Diana

PY - 2014

Y1 - 2014

N2 - Wang et al. (Reports, 4 October 2013, p. 127) claimed high prediction power for their model of citation dynamics. We replicate their analysis but find discouraging results: 14.75% papers are estimated with unreasonably large μ (>5) and λ (>10) and correspondingly enormous prediction errors. The prediction power is even worse than simply using short-term citations to approximate long-term citations.

AB - Wang et al. (Reports, 4 October 2013, p. 127) claimed high prediction power for their model of citation dynamics. We replicate their analysis but find discouraging results: 14.75% papers are estimated with unreasonably large μ (>5) and λ (>10) and correspondingly enormous prediction errors. The prediction power is even worse than simply using short-term citations to approximate long-term citations.

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JO - Science

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Comment on "Quantifying long-term scientific impact"

Abstract

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