Estimating Number of Citations Using Author Reputation

Castillo, Carlos; Donato, Debora; Gionis, Aristides

doi:10.1007/978-3-540-75530-2_10

Cited by 64 publications

(60 citation statements)

References 11 publications

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“…We also find further evidence of the Matthew effect by analyzing the interpublication time τðxÞ that decreases with increasing publication x for individual authors within a given journal. Thus, we conclude that publication in a particular journal is facilitated by previous publications in the journal, corresponding to an increasing reputation within the given journal (22). Several other metrics for quantifying career success (18,23), such as the h index (17) and generalizations (24,25), along with methods for removing time-and discipline-dependent citation factors (26) have been analyzed in the spirit of developing unbiased rating systems for scientific achievement.…”

Section: Empirical Evidencementioning

confidence: 99%

Quantitative and empirical demonstration of the Matthew effect in a study of career longevity

Petersen

Jung

Yang

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

225

239

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The Matthew effect refers to the adage written some two-thousand years ago in the Gospel of St. Matthew: "For to all those who have, more will be given." Even two millennia later, this idiom is used by sociologists to qualitatively describe the dynamics of individual progress and the interplay between status and reward. Quantitative studies of professional careers are traditionally limited by the difficulty in measuring progress and the lack of data on individual careers. However, in some professions, there are well-defined metrics that quantify career longevity, success, and prowess, which together contribute to the overall success rating for an individual employee. Here we demonstrate testable evidence of the age-old Matthew "rich get richer" effect, wherein the longevity and past success of an individual lead to a cumulative advantage in further developing his or her career. We develop an exactly solvable stochastic career progress model that quantitatively incorporates the Matthew effect and validate our model predictions for several competitive professions. We test our model on the careers of 400,000 scientists using data from six high-impact journals and further confirm our findings by testing the model on the careers of more than 20,000 athletes in four sports leagues. Our model highlights the importance of early career development, showing that many careers are stunted by the relative disadvantage associated with inexperience. T he rate of individual progress is fundamental to career development and success. In practice, the rate of progress depends on many factors, such as an individual's talent, productivity, reputation, as well as other external random factors. Using a stochastic model, here we find that the relatively small rate of progress at the beginning of the career plays a crucial role in the evolution of the career length. Our quantitative model describes career progression using two fundamental ingredients: (i) random forward progress "up the career ladder" and (ii) random stopping times, terminating a career. This model quantifies the "Matthew effect" by incorporating into ingredient (i) the common cumulative advantage property (1-8) that it is easier to move forward in the career the further along one is in the career. A direct result of the increasing progress rate with career position is the large disparity between the numbers of careers that are successful long tenures and the numbers of careers that are unsuccessful short stints.Surprisingly, despite the large differences in the numbers of long and short careers, we find a scaling law that bridges the gap between the frequent short and the infrequent long careers. We test this model for both scientific and sports careers, two careers where accomplishments are methodically recorded. We analyze publication careers within six high-impact journals: Nature, Science, the Proceedings of the National Academy of Science (PNAS), Physical Review Letters (PRL), New England Journal of Medicine (NEJM), and CELL. We also analyze sports careers within ...

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Section: Empirical Evidencementioning

confidence: 99%

Quantitative and empirical demonstration of the Matthew effect in a study of career longevity

Petersen

Jung

Yang

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

225

239

View full text Add to dashboard Cite

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“…There are empirical proofs of the fact that the citations received by a new article are generally consistent with the citations received by previous articles of the same author, that is to say the author's reputation (Castillo et al 2007). Being the number of authors per journal quite large (typically more than 40-60 authors per year), it is reasonable to assume that the authors' reputation will be generally respected.…”

Section: Further Reflections On the H-spectrummentioning

confidence: 99%

“…It must be pointed out that citation accumulation of one article requires a certain amount of time to become stable. According to some authors, about 5-6 years for journals in the management/ engineering field (Amin and Mabe 2000;Castillo et al 2007;Harzing 2010). This ''physiological'' behaviour is well represented in Figs.…”

Section: Total Number Of Citationsmentioning

confidence: 99%

Bibliometric positioning of scientific manufacturing journals: a comparative analysis

Franceschini

Maisano

2010

Scientometrics

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This article analyzes some of the most popular scientific journals in the Manufacturing field from the point of view of four bibliometric indicators: the ISI impact factor (ISI-IF), the Hirsch (h) index-for-journal, the total number of citations and the h-spectrum. h-spectrum is a novel tool based on h, making it possible to (i) identify a reference profile of the typical authors of a journal, (ii) compare different journals and (iii) provide a rough indication of their ''bibliometric positioning'' in the scientific community. Results of this analysis can be helpful for guiding potential authors and members of the scientific community in the Manufacturing area. Of particular interest is the construction of maps based on h-spectrum and ISI-IF to compare journals and monitor their bibliometric positioning over time. A large amount of empirical data are presented and discussed.

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“…These include citations of a paper soon after it is published [10,11]; measures of network centrality such as average shortest path length, clustering coefficient and betweenness centrality [12]; the paper's authors' previous work [13,14]; and keywords from the text of the paper [15]. The framework of information diffusion emphasises that ideas, like epidemics, spread through networks [16,17].…”

Section: Related Workmentioning

confidence: 99%

Predicting High Impact Academic Papers Using Citation Network Features

McNamara

Wong

Christen

et al. 2013

Lecture Notes in Computer Science

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Abstract. Predicting future high impact academic papers is of benefit to a range of stakeholders, including governments, universities, academics, and investors. Being able to predict 'the next big thing' allows the allocation of resources to fields where these rapid developments are occurring. This paper develops a new method for predicting a paper's future impact using features of the paper's neighbourhood in the citation network, including measures of interdisciplinarity. Predictors of high impact papers include high early citation counts of the paper, high citation counts by the paper, citations of and by highly cited papers, and interdisciplinary citations of the paper and of papers that cite it. The Scopus database, consisting of over 24 million publication records from 1996-2010 across a wide range of disciplines, is used to motivate and evaluate the methods presented.

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Estimating Number of Citations Using Author Reputation

Cited by 64 publications

References 11 publications

Quantitative and empirical demonstration of the Matthew effect in a study of career longevity

Quantitative and empirical demonstration of the Matthew effect in a study of career longevity

Bibliometric positioning of scientific manufacturing journals: a comparative analysis

Predicting High Impact Academic Papers Using Citation Network Features

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