Search (161 results, page 2 of 9)

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h-index

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h-index

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Abstract

Recently, it was shown that among existing theoretical models for the h-index, the Glänzel-Schubert model provides the best fit for a chosen example involving the research evaluation of universities. In this brief communication, we propose a thermodynamic explanation for the success of the Glänzel-Schubert model of the h-index.

Object

h-index

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Abstract

The h-index has been shown to increase in many cases mostly because of citations to rather old publications. This inertia can be circumvented by restricting the evaluation to a citation time window. Here I report results of an empirical study analyzing the evolution of the thus defined timed h-index in dependence on the length of the citation time window.

Object

h-index

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Date

22. 1.2017 17:12:50

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Date

22. 8.2014 17:05:18

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Date

20. 1.2019 11:22:31

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Abstract

The h-index was devised to represent a scholar's contributions to his field with respect to the number of publications and citations. It does not, however, take into consideration the scholar's position in the authorship list. I recommend a new supplementary index to score academics, representing the relative contribution to the papers with impact, be reported alongside the h-index. I call this index the AP-index, and it is simply defined as the average position in which an academic appears in authorship lists, on articles that factor in to that academic's h-index.

Object

h-index

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Abstract

Various mathematical models have been proposed in the recent literature for estimating the h-index using measures such as number of articles (P) and citations received (C). These models have been previously empirically tested assuming a mathematical model and predetermining the models' parameter values at some fixed constant. The present study, from a statistical modeling viewpoint, investigates alternative distributions commonly used for this type of point data. The study shows that the typical assumptions for the parameters of the h-index mathematical models in such representations are not always realistic, with more suitable specifications being favorable. Prediction of the h-index is also demonstrated.

Object

h-index

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Abstract

The h-index provides us with 9 natural classes which can be written as a matrix of 3 vectors. The 3 vectors are: X = (X1, X2, X3) and indicates publication distribution in the h-core, the h-tail, and the uncited ones, respectively; Y = (Y1, Y2, Y3) denotes the citation distribution of the h-core, the h-tail and the so-called "excess" citations (above the h-threshold), respectively; and Z = (Z1, Z2, Z3) = (Y1-X1, Y2-X2, Y3-X3). The matrix V = (X,Y,Z)T constructs a measure of academic performance, in which the 9 numbers can all be provided with meanings in different dimensions. The "academic trace" tr(V) of this matrix follows naturally, and contributes a unique indicator for total academic achievements by summarizing and weighting the accumulation of publications and citations. This measure can also be used to combine the advantages of the h-index and the integrated impact indicator (I3) into a single number with a meaningful interpretation of the values. We illustrate the use of tr(V) for the cases of 2 journal sets, 2 universities, and ourselves as 2 individual authors.

Object

h-index

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Abstract

In this work we develop new journal classification methods based on the h-index. The introduction of the h-index for research evaluation has attracted much attention in the bibliometric study and research quality evaluation. The main purpose of using an h-index is to compare the index for different research units (e.g. researchers, journals, etc.) to differentiate their research performance. However the h-index is defined by only comparing citations counts of one's own publications, it is doubtful that the h index alone should be used for reliable comparisons among different research units, like researchers or journals. In this paper we propose a new global h-index (Gh-index), where the publications in the core are selected in comparison with all the publications of the units to be evaluated. Furthermore, we introduce some variants of the Gh-index to address the issue of discrimination power. We show that together with the original h-index, they can be used to evaluate and classify academic journals with some distinct advantages, in particular that they can produce an automatic classification into a number of categories without arbitrary cut-off points. We then carry out an empirical study for classification of operations research and management science (OR/MS) journals using this index, and compare it with other well-known journal ranking results such as the Association of Business Schools (ABS) Journal Quality Guide and the Committee of Professors in OR (COPIOR) ranking lists.

Object

h-index

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Abstract

Adding or deleting items such as self-citations has an influence on the h-index of an author. This influence will be proved mathematically in this article. We hereby prove the experimental finding in E. Gianoli and M.A. Molina-Montenegro ([2009]) that the influence of adding or deleting self-citations on the h-index is greater for low values of the h-index. Why this is logical also is shown by a simple theoretical example. Adding or deleting sources such as adding or deleting minor contributions of an author also has an influence on the h-index of this author; this influence is modeled in this article. This model explains some practical examples found in X. Hu, R. Rousseau, and J. Chen (in press).

Object

h-index

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Abstract

The use of Hirsch's h-index as a joint proxy of the impact and productivity of a scientist's research work continues to gain ground, accompanied by the efforts of bibliometrists to resolve some of its critical issues through the application of a number of more or less sophisticated variants. However, the literature does not reveal any appreciable attempt to overcome the objective problems of measuring h-indexes on a large scale for purposes of comparative evaluation. Scientists may succeed in calculating their own h-indexes but, being unable to compare them to those of their peers, they are unable to obtain truly useful indications of their individual research performance. This study proposes to overcome this gap, measuring the h- and Egghe's g-indexes of all Italian university researchers in the hard sciences over a 5-year window. Descriptive statistics are provided concerning all of the 165 subject fields examined, offering robust benchmarks for those who wish to compare their individual performance to those of their colleagues in the same subject field.

Object

h-index

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Abstract

Most measures of networks are based on the nodes, although links are also elementary units in networks and represent interesting social or physical connections. In this work we suggest an option for exploring networks, called the h-strength, with explicit focus on links and their strengths. The h-strength and its extensions can naturally simplify a complex network to a small and concise subnetwork (h-subnet) but retains the most important links with its core structure. Its applications in 2 typical information networks, the paper cocitation network of a topic (the h-index) and 5 scientific collaboration networks in the field of "water resources," suggest that h-strength and its extensions could be a useful choice for abstracting, simplifying, and visualizing a complex network. Moreover, we observe that the 2 informetric models, the Glänzel-Schubert model and the Hirsch model, roughly hold in the context of the h-strength for the collaboration networks.

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Abstract

This study employs a panel threshold regression model to test whether the patent h-index has a threshold effect on the relationship between patent citations and market value in the pharmaceutical industry. It aims to bridge the gap in extant research on this topic. This study demonstrates that the patent h-index has a triple threshold effect on the relationship between patent citations and market value. When the patent h-index is less than or equal to the lowest threshold, 4, there is a positive relationship between patent citations and market value. This study indicates that the first regime (where the patent h-index is less than or equal to 4) is optimal, because this is where the extent of the positive relationship between patent citations and market value is the greatest.

Object

h-index

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h-index

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Abstract

Purpose The purpose of this paper is to show how bibliometrics would benefit from a stronger programme of construct validity. Design/methodology/approach The value of the construct validity concept is demonstrated by applying this approach to the evaluation of the h-index, a widely used metric. Findings The paper demonstrates that the h-index comprehensively fails any test of construct validity. In simple terms, the metric does not measure what it purports to measure. This conclusion suggests that the current popularity of the h-index as a topic for bibliometric research represents wasted effort, which might have been avoided if researchers had adopted the approach suggested in this paper. Research limitations/implications This study is based on the analysis of a single bibliometric concept. Practical implications The conclusion that the h-index fails any test in terms of construct validity implies that the widespread use of this metric within the higher education sector as a management tool represents poor practice, and almost certainly results in the misallocation of resources. Social implications This paper suggests that the current enthusiasm for the h-index within the higher education sector is misplaced. The implication is that universities, grant funding bodies and faculty administrators should abandon the use of the h-index as a management tool. Such a change would have a significant effect on current hiring, promotion and tenure practices within the sector, as well as current attitudes towards the measurement of academic performance. Originality/value The originality of the paper lies in the systematic application of the concept of construct validity to bibliometric enquiry.

Object

h-index

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Abstract

This article studies the impact of differences in citation practices at the subfield, or Web of Science subject category level, using the model introduced in Crespo, Li, and Ruiz-Castillo (2013a), according to which the number of citations received by an article depends on its underlying scientific influence and the field to which it belongs. We use the same Thomson Reuters data set of about 4.4 million articles used in Crespo et al. (2013a) to analyze 22 broad fields. The main results are the following: First, when the classification system goes from 22 fields to 219 subfields the effect on citation inequality of differences in citation practices increases from ?14% at the field level to 18% at the subfield level. Second, we estimate a set of exchange rates (ERs) over a wide [660, 978] citation quantile interval to express the citation counts of articles into the equivalent counts in the all-sciences case. In the fractional case, for example, we find that in 187 of 219 subfields the ERs are reliable in the sense that the coefficient of variation is smaller than or equal to 0.10. Third, in the fractional case the normalization of the raw data using the ERs (or subfield mean citations) as normalization factors reduces the importance of the differences in citation practices from 18% to 3.8% (3.4%) of overall citation inequality. Fourth, the results in the fractional case are essentially replicated when we adopt a multiplicative approach.

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Date

26.10.2014 20:22:22

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Date

20. 1.2015 18:30:22
17. 9.2018 18:22:23