Diffusion of scientific credits and the ranking of scientists

Radicchi, Filippo; Fortunato, Santo; Markines, Benjamin; Vespignani, Alessandro

doi:10.1103/physreve.80.056103

Cited by 272 publications

(249 citation statements)

References 20 publications

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“…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.

229

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.

229

239

View full text Add to dashboard Cite

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“…There are several metrics of individual impact. Many of these are ranking measures providing quantitative estimates of the relative importance of a scientist [10]. The recently introduced h-index [11], which combines the impact of the papers of a scientist with his/her productivity, is by far the most popular.…”

Section: Figure 1 -Using Citation Analysis To Measure Research Impactmentioning

confidence: 99%

Current Biomedical Scientific Impact (2013) of Institutions, Academic Journals and Researchers in the Republic of Macedonia

Спироски

2014

Prilozi

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“…Nor do we consider indices accounting for the quality of the citations in terms of the collaboration distance between citing and cited authors [47], as we do not model the presence of research groups. Finally, we do not consider metrics based on the eigenvector centrality within the citation network [48], such as PageRank [49], CiteRank [49], or PhysAuthorRank [50]. This is because the linking probability defined by Eq.…”

Section: Impact Indicatorsmentioning

confidence: 99%

Model-based evaluation of scientific impact indicators

Medo

Cimini

2016

Phys. Rev. E

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Using bibliometric data artificially generated through a model of citation dynamics calibrated on empirical data, we compare several indicators for the scientific impact of individual researchers. The use of such a controlled setup has the advantage of avoiding the biases present in real databases, and it allows us to assess which aspects of the model dynamics and which traits of individual researchers a particular indicator actually reflects. We find that the simple average citation count of the authored papers performs well in capturing the intrinsic scientific ability of researchers, regardless of the length of their career. On the other hand, when productivity complements ability in the evaluation process, the notorious h and g indices reveal their potential, yet their normalized variants do not always yield a fair comparison between researchers at different career stages. Notably, the use of logarithmic units for citation counts allows us to build simple indicators with performance equal to that of h and g. Our analysis may provide useful hints for a proper use of bibliometric indicators. Additionally, our framework can be extended by including other aspects of the scientific production process and citation dynamics, with the potential to become a standard tool for the assessment of impact metrics.

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Diffusion of scientific credits and the ranking of scientists

Cited by 272 publications

References 20 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

Current Biomedical Scientific Impact (2013) of Institutions, Academic Journals and Researchers in the Republic of Macedonia

Model-based evaluation of scientific impact indicators

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