2,172 research outputs found
Statistical regularities in the rank-citation profile of scientists
Recent science of science research shows that scientific impact measures for journals and individual articles have quantifiable regularities across both time and discipline. However, little is known about the scientific impact distribution at the scale of an individual scientist. We analyze the aggregate production and impact using the rank-citation profile ci(r) of 200 distinguished professors and 100 assistant professors. For the entire range of paper rank r, we fit each ci(r) to a common distribution function. Since two scientists with equivalent Hirsch h-index can have significantly different ci(r) profiles, our results demonstrate the utility of the βi scaling parameter in conjunction with hi for quantifying individual publication impact. We show that the total number of citations Ci tallied from a scientist's Ni papers scales as . Such statistical regularities in the input-output patterns of scientists can be used as benchmarks for theoretical models of career progress
The Z-index: A geometric representation of productivity and impact which accounts for information in the entire rank-citation profile
We present a simple generalization of Hirsch's h-index, Z =
\sqrt{h^{2}+C}/\sqrt{5}, where C is the total number of citations. Z is aimed
at correcting the potentially excessive penalty made by h on a scientist's
highly cited papers, because for the majority of scientists analyzed, we find
the excess citation fraction (C-h^{2})/C to be distributed closely around the
value 0.75, meaning that 75 percent of the author's impact is neglected.
Additionally, Z is less sensitive to local changes in a scientist's citation
profile, namely perturbations which increase h while only marginally affecting
C. Using real career data for 476 physicists careers and 488 biologist careers,
we analyze both the distribution of and the rank stability of Z with
respect to the Hirsch index h and the Egghe index g. We analyze careers
distributed across a wide range of total impact, including top-cited physicists
and biologists for benchmark comparison. In practice, the Z-index requires the
same information needed to calculate h and could be effortlessly incorporated
within career profile databases, such as Google Scholar and ResearcherID.
Because Z incorporates information from the entire publication profile while
being more robust than h and g to local perturbations, we argue that Z is
better suited for ranking comparisons in academic decision-making scenarios
comprising a large number of scientists.Comment: 9 pages, 5 figure
Reputation and Impact in Academic Careers
Reputation is an important social construct in science, which enables
informed quality assessments of both publications and careers of scientists in
the absence of complete systemic information. However, the relation between
reputation and career growth of an individual remains poorly understood,
despite recent proliferation of quantitative research evaluation methods. Here
we develop an original framework for measuring how a publication's citation
rate depends on the reputation of its central author , in
addition to its net citation count . To estimate the strength of the
reputation effect, we perform a longitudinal analysis on the careers of 450
highly-cited scientists, using the total citations of each scientist as
his/her reputation measure. We find a citation crossover which
distinguishes the strength of the reputation effect. For publications with , the author's reputation is found to dominate the annual citation
rate. Hence, a new publication may gain a significant early advantage
corresponding to roughly a 66% increase in the citation rate for each tenfold
increase in . However, the reputation effect becomes negligible for
highly cited publications meaning that for the citation rate
measures scientific impact more transparently. In addition we have developed a
stochastic reputation model, which is found to reproduce numerous statistical
observations for real careers, thus providing insight into the microscopic
mechanisms underlying cumulative advantage in science.Comment: Final published version of the main manuscript including additional
analysis: 9 pages, 4 figures, 1 table, and full reference list, including
those in the Supplementary Information. For the SI Appendix, see
http://physics.bu.edu/~amp17/webpage_files/MyPapers/Reputation_SI.pd
Inequality and cumulative advantage in science careers: a case study of high-impact journals
Analyzing a large data set of publications drawn from the most competitive journals in the natural and social sciences we show that research careers exhibit the broad distributions of individual achievement characteristic of systems in which cumulative advantage plays a key role. While most researchers are personally aware of the competition implicit in the publication process, little is known about the levels of inequality at the level of individual researchers. Here we analyzed both productivity and impact measures for a large set of researchers publishing in high-impact journals, accounting for censoring biases in the publication data by using distinct researcher cohorts defined over non-overlapping time periods. For each researcher cohort we calculated Gini inequality coefficients, with average Gini values around 0.48 for total publications and 0.73 for total citations. For perspective, these observed values are well in excess of the inequality levels observed for personal income in developing countries. Investigating possible sources of this inequality, we identify two potential mechanisms that act at the level of the individual that may play defining roles in the emergence of the broad productivity and impact distributions found in science. First, we show that the average time interval between a researcher’s successive publications in top journals decreases with each subsequent publication. Second, after controlling for the time dependent features of citation distributions, we compare the citation impact of subsequent publications within a researcher’s publication record. We find that as researchers continue to publish in top journals, there is more likely to be a decreasing trend in the relative citation impact with each subsequent publication. This pattern highlights the difficulty of repeatedly producing research findings in the highest citation-impact echelon, as well as the role played by finite career and knowledge life-cycles, and the intriguing possibility that confirmation bias plays a role in the evaluation of scientific careers
The Distribution of the Asymptotic Number of Citations to Sets of Publications by a Researcher or From an Academic Department Are Consistent With a Discrete Lognormal Model
How to quantify the impact of a researcher's or an institution's body of work
is a matter of increasing importance to scientists, funding agencies, and
hiring committees. The use of bibliometric indicators, such as the h-index or
the Journal Impact Factor, have become widespread despite their known
limitations. We argue that most existing bibliometric indicators are
inconsistent, biased, and, worst of all, susceptible to manipulation. Here, we
pursue a principled approach to the development of an indicator to quantify the
scientific impact of both individual researchers and research institutions
grounded on the functional form of the distribution of the asymptotic number of
citations. We validate our approach using the publication records of 1,283
researchers from seven scientific and engineering disciplines and the chemistry
departments at the 106 U.S. research institutions classified as "very high
research activity". Our approach has three distinct advantages. First, it
accurately captures the overall scientific impact of researchers at all career
stages, as measured by asymptotic citation counts. Second, unlike other
measures, our indicator is resistant to manipulation and rewards publication
quality over quantity. Third, our approach captures the time-evolution of the
scientific impact of research institutions.Comment: 20 pages, 11 figures, 3 table
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