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Abstract

Many studies (in information science) have looked at the growth of science. In this study, we reexamine the question of the growth of science. To do this we (a) use current data up to publication year 2012 and (b) analyze the data across all disciplines and also separately for the natural sciences and for the medical and health sciences. Furthermore, the data were analyzed with an advanced statistical technique-segmented regression analysis-which can identify specific segments with similar growth rates in the history of science. The study is based on two different sets of bibliometric data: (a) the number of publications held as source items in the Web of Science (WoS, Thomson Reuters) per publication year and (b) the number of cited references in the publications of the source items per cited reference year. We looked at the rate at which science has grown since the mid-1600s. In our analysis of cited references we identified three essential growth phases in the development of science, which each led to growth rates tripling in comparison with the previous phase: from less than 1% up to the middle of the 18th century, to 2 to 3% up to the period between the two world wars, and 8 to 9% to 2010.

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Abstract

It is essential for research funding organizations to ensure both the validity and fairness of the grant approval procedure. The ex-ante peer evaluation (EXANTE) of N?=?8,496 grant applications submitted to the Austrian Science Fund from 1999 to 2009 was statistically analyzed. For 1,689 funded research projects an ex-post peer evaluation (EXPOST) was also available; for the rest of the grant applications a multilevel missing data imputation approach was used to consider verification bias for the first time in peer-review research. Without imputation, the predictive validity of EXANTE was low (r?=?.26) but underestimated due to verification bias, and with imputation it was r?=?.49. That is, the decision-making procedure is capable of selecting the best research proposals for funding. In the EXANTE there were several potential biases (e.g., gender). With respect to the EXPOST there was only one real bias (discipline-specific and year-specific differential prediction). The novelty of this contribution is, first, the combining of theoretical concepts of validity and fairness with a missing data imputation approach to correct for verification bias and, second, multilevel modeling to test peer review-based funding decisions for both validity and fairness in terms of potential and real biases.

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Date

22. 8.2014 17:05:18

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Abstract

In this study, we examined empirical results on the h index and its most important variants in order to determine whether the variants developed are associated with an incremental contribution for evaluation purposes. The results of a factor analysis using bibliographic data on postdoctoral researchers in biomedicine indicate that regarding the h index and its variants, we are dealing with two types of indices that load on one factor each. One type describes the most productive core of a scientist's output and gives the number of papers in that core. The other type of indices describes the impact of the papers in the core. Because an index for evaluative purposes is a useful yardstick for comparison among scientists if the index corresponds strongly with peer assessments, we calculated a logistic regression analysis with the two factors resulting from the factor analysis as independent variables and peer assessment of the postdoctoral researchers as the dependent variable. The results of the regression analysis show that peer assessments can be predicted better using the factor impact of the productive core than using the factor quantity of the productive core.

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Abstract

Since the 1990s, with the heightened competition and the strong growth of the international higher education market, an increasing number of rankings have been created that measure the scientific performance of an institution based on data. The Leiden Ranking 2011/2012 (LR) was published early in 2012. Starting from Goldstein and Spiegelhalter's (1996) recommendations for conducting quantitative comparisons among institutions, in this study we undertook a reformulation of the LR by means of multilevel regression models. First, with our models we replicated the ranking results; second, the reanalysis of the LR data showed that only 5% of the PPtop10% total variation is attributable to differences between universities. Beyond that, about 80% of the variation between universities can be explained by differences among countries. If covariates are included in the model the differences among most of the universities become meaningless. Our findings have implications for conducting university rankings in general and for the LR in particular. For example, with Goldstein-adjusted confidence intervals, it is possible to interpret the significance of differences among universities meaningfully: Rank differences among universities should be interpreted as meaningful only if their confidence intervals do not overlap.

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Abstract

We submit newly developed citation impact indicators based not on arithmetic averages of citations but on percentile ranks. Citation distributions are-as a rule-highly skewed and should not be arithmetically averaged. With percentile ranks, the citation score of each paper is rated in terms of its percentile in the citation distribution. The percentile ranks approach allows for the formulation of a more abstract indicator scheme that can be used to organize and/or schematize different impact indicators according to three degrees of freedom: the selection of the reference sets, the evaluation criteria, and the choice of whether or not to define the publication sets as independent. Bibliometric data of seven principal investigators (PIs) of the Academic Medical Center of the University of Amsterdam are used as an exemplary dataset. We demonstrate that the proposed family indicators [R(6), R(100), R(6, k), R(100, k)] are an improvement on averages-based indicators because one can account for the shape of the distributions of citations over papers.

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Abstract

Using data compiled for the SCImago Institutions Ranking, we look at whether the subject area type an institution (university or research-focused institution) belongs to (in terms of the fields researched) has an influence on its ranking position. We used latent class analysis to categorize institutions based on their publications in certain subject areas. Even though this categorization does not relate directly to scientific performance, our results show that it exercises an important influence on the outcome of a performance measurement: Certain subject area types of institutions have an advantage in the ranking positions when compared with others. This advantage manifests itself not only when performance is measured with an indicator that is not field-normalized but also for indicators that are field-normalized.

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Abstract

Scientific journals publish an increasing number of articles every year. To steer readers' attention to the most important papers, journals use several techniques (e.g., lead paper). Angewandte Chemie International Edition (AC), a leading international journal in chemistry, signals high-quality papers through designating them as a "very important paper" (VIP). This study aims to investigate the citation impact of Communications in AC receiving the special feature VIP, both cumulated and over time. Using propensity score matching, treatment group (VIP) and control group (non-VIP) were balanced for 14 covariates to estimate the unconfounded "average treatment effect on the treated" for the VIP designation. Out of N = 3,011 Communications published in 2007 and 2008, N = 207 received the special feature VIP. For each Communication, data were collected from AC (e.g., referees' ratings) and from the databases Chemical Abstracts (e.g., sections) and the Web of Science (e.g., citations). The estimated unconfounded average treatment effect on the treated (that is, Communications designated as a VIP) was statistically significant and amounted to 19.83 citations. In addition, the special feature VIP fostered the cumulated annual citation growth. For instance, the time until a Communication reached its maximum annual number of citations, was reduced.